∎

11institutetext: XQ Zhang, ZJ Xiao, Y Hu, JS Fang, R Higashita
22institutetext: J Liu 33institutetext: Research Institute of Trustworthy
Autonomous Systems and Department of Computer Science and Engineering,
Southern University of Science and Technology, Shenzhen, China
33email<EMAIL_ADDRESS>
# Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging
Modalities: A Survey

Xiaoqing Zhang  Yan Hu  Zunjie Xiao  Jiansheng Fang  Risa Higashita  Jiang Liu
∗ ∗ denotes corresponding author

(Received: date / Accepted: date)

###### Abstract

Cataracts are the leading cause of visual impairment and blindness globally.
Over the years, researchers have achieved significant progress in developing
state-of-the-art machine learning techniques for automatic cataract
classification and grading, aiming to prevent cataracts early and improve
clinicians’ diagnosis efficiency. This survey provides a comprehensive survey
of recent advances in machine learning techniques for cataract
classification/grading based on ophthalmic images. We summarize existing
literature from two research directions: conventional machine learning methods
and deep learning methods. This survey also provides insights into existing
works of both merits and limitations. In addition, we discuss several
challenges of automatic cataract classification/grading based on machine
learning techniques and present possible solutions to these challenges for
future research.

###### Keywords:

Cataract, classification and grading, ophthalmic image, machine learning, deep
learning

##  1 Introduction

Figure 1: Overall organization framework of this survey.

According to World Health Organization (WHO) Bourne et al. (2017); Pascolini
and Mariotti (2012), it is estimated that approximately 2.2 billion people
suffer visual impairment. Cataract accounts for about 33% of visual impairment
and is the number one cause of blindness (over 50%) worldwide. Cataract
patients can improve life quality and vision through early intervention and
cataract surgery, which are efficient methods to reduce blindness ratio and
cataract-blindness burden for society simultaneously.

Clinically, cataracts are the loss of crystalline lens transparency, which
occur when the protein inside the lens clumps together Asbell et al. (2005).
They are associated with many factors Liu et al. (2017b), such as
developmental abnormalities, trauma, metabolic disorders, genetics, drug-
induced changes, ages, etc. Genetics and aging are two of the most important
factors for cataracts. According to the causes of cataracts, they can be
categorized as age-related cataract, pediatrics cataract (PC), and secondary
cataract Asbell et al. (2005); Liu et al. (2017b). According to the location
of the crystalline lens opacity, they can be grouped into nuclear cataract
(NC), cortical cataract (CC), and posterior subcapsular cataract (PSC) Li et
al. (2009, 2010b). NC denotes the gradual clouding and the progressive
hardening in the nuclear region. CC is the form of white wedged-shaped and
radially oriented opacities, and it develops from the outside edge of the lens
toward the center in a spoke-like fashion Liu et al. (2017b); Chew et al.
(2012). PSC is granular opacities, and its symptom includes small breadcrumbs
or sand particles, which are sprinkled beneath the lens capsule Li et al.
(2010b).

Over the past years, ophthalmologists have used several ophthalmic images to
diagnose cataract based on their experience and clinical training. This manual
diagnosis mode is error-prone, time-consuming, subjective, and costly, which
is a great challenge in developing countries or rural communities, where
experienced clinicians are scarce. To prevent cataract early and improve the
precision and efficiency of cataract diagnosis, researchers have made great
efforts in developing computer-aided diagnosis (CAD) techniques for automatic
cataract classification/grading Long et al. (2017) on different ophthalmic
images, including conventional machine learning methods and deep learning
methods. The conventional machine learning method is a combination of feature
extraction and classification/grading. In the feature extraction stage, a
variety of image processing methods have been proposed to obtain visual
features of cataract according to different ophthalmic images, such as
density-based statistics method, density histogram method, bag-of-features
(BOF) method, Gabor Wavelet transform, Gray level Cooccurrence Matrix (GLCM),
Haar wavelet transform, etc Huang et al. (2009b); Xu et al. (2016); Li et al.
(2010b); Xu et al. (2013); Gao et al. (2011); Srivastava et al. (2014);
Patwari et al. (2011); Fuadah et al. (2015); Pathak and Kumar (2016). In the
classification/grading stage, strong classification methods are applied to
recognize different cataract severity levels, e.g., support vector machine
(SVM) Khan et al. (2018); Yang et al. (2016); Qiao et al. (2017). Over the
past ten years, deep learning has achieved great success in various fields,
including medical image analysis, which can be viewed as a representation
learning approach. It can learn low-, mid-, and high-level feature
representations from raw data in an end-to-end manner (e.g., ophthalmic
images). In the recent, various deep neural networks have been utilized to
tackle cataract classification/grading tasks like convolutional neural
networks (CNNs), attention-based networks, Faster-RCNN and multilayer
perceptron neural networks (MLP). E.g., Zhang et al. Zhang et al. (2022c)
proposed a multi-region fusion attention network to recognize nuclear cataract
severity levels.

Previous surveys had summarized cataract types, cataract
classification/grading systems, and ophthalmic imaging modalities,
respectively Zhang et al. (2014); Liu et al. (2017b); Shaheen and Tariq
(2019); Lopez et al. (2016); Zafar et al. (2018); Gali et al. (2019); Goh et
al. (2020); however, none had summarized ML techniques based on ophthalmic
imaging modalities for automatic cataract classification/ grading
systematically. To the best of our knowledge, this is the first survey that
systematically summarizes recent advances in ML techniques for automatic
cataract classification/ grading. This survey mainly focuses on surveying ML
techniques in cataract classification/grading, comprised of conventional ML
methods and deep learning methods. We survey these published papers through
Web of Science (WoS), Scopus, and Google Scholar databases. Fig. 1 provides a
general organization framework for this survey according to collected papers,
our summary, and discussion with experienced ophthalmologists. To understand
this survey easily, we also review ophthalmic imaging modalities, cataract
grading systems, and commonly-used evaluation measures in brief. Then we
introduce ML techniques step by step. We hope this survey can provide a
valuable summary of current works and present potential research directions of
ML-based cataract classification/grading in the future.

##  2 Ophthalmic imaging modalities for cataract classification/grading

To our best understanding, this survey introduces six different eye images
used for cataract classification/grading for the first time: slit-lamp image,
retroillumination image, ultrasonic image, fundus image, digital camera image,
and anterior segment optical coherence tomography (AS-OCT) image, as shown in
Fig. 2. In the following section, we will introduce each ophthalmic image type
step by step and then discuss their advantages and disadvantages.

Figure 2: Six different ophthalmic images. (a) Slit lamp image; (b)
Retroillumination image; (c) Ultrasonic image; (d) Fundus image (e) Digital
camera image (f) Anterior segment optical coherence tomography image.

###  2.1 Slit lamp image

The slit lamp camera Fercher et al. (1993); Waltman and Kaufman (1970) is a
high-intensity light source instrument, which is comprised of the corneal
microscope and the slit lamp. Silt lamp image can be accessed through slit
lamp camera, which is usually used to examine the anterior segment and
posterior segment structure of the human eye eyelid, sclera, conjunctiva,
iris, crystalline lens, and cornea. Fig. 3 offers four representative slit
lamp images for four different cataract severity levels.

Figure 3: Slit lamp images with four nuclear cataract severity levels.

###  2.2 Retroillumination image

Retroillumination image is a non-stereoscopic medical image, which is accessed
through the crystalline lens camera Vivino et al. (1995); Gershenzon and
Robman (1999). It can be used to diagnose CC and PSC in the crystalline lens
region. Two types of retroillumination images through the crystalline lens
camera can be obtained: an anterior image focused on the iris, which
corresponds to the anterior cortex of the lens, and a posterior image focused
on 3-5mm more posteriorly, which intends to image the opacity of PSC.

###  2.3 Ultrasonic image

In clinical cataract diagnosis, Ultrasound image is a commonly-used ophthalmic
image modality to evaluate the hardness of cataract lens objectively Huang et
al. (2009a). Frequently applied Ultrasound imaging techniques usually are
developed based on measuring ultrasonic attenuation and sound speed, which may
increase the hardness of the cataract lens Huang et al. (2007). High-frequency
Ultrasound B-mode imaging can be used to monitor local cataract formation, but
it cannot measure the lens hardness accurately Tsui and Chang (2007). To make
up for the B-scan deficiency, the Ultrasound imaging technique built on
Nakagami statistical model called Ultrasonic Nakagami imaging Tsui et al.
(2010, 2007, 2016) was developed, which can be used for the visualization of
local scatterer concentrations in biological tissues.

Figure 4:  Four cataract severity levels on fundus images Cao et al. (2020b).
(a) Normal; (b) Immature; (c) Mature; (d) Hypermature

###  2.4 Fundus image

The fundus camera Plesch et al. (1987); Pomerantzeff et al. (1979) is a unique
camera in conjunction with a low power microscope, which is usually used to
capture fundus images operated by ophthalmologists or professional operators.
Fundus image is a highly specialized form of eye imaging and can capture the
eye’s inner lining and the structures of the back of the eye. Fig. 4 shows
four fundus images of different cataract severity levels.

###  2.5 Digital camera image

Commonly used digital cameras can access digital camera images like smartphone
cameras. Compared with the fundus camera and slit lamp device, the digital
camera is easily available and easily used. Hence, using digital cameras for
cataract screening has great potential in the future, especially for
developing countries and rural areas, where people have limitations to access
expensive ophthalmology equipment and experienced ophthalmologists.

###  2.6 Anterior segment optical coherence tomography image

Anterior segment optical coherence tomography (AS-OCT) Ang et al. (2018)
imaging technique is one of optical coherence tomography (OCT) imaging
techniques. It can be used to visualize and assess anterior segment ocular
features, such as the tear film, cornea, conjunctiva, sclera, rectus muscles,
anterior chamber angle structures, and lens Werkmeister et al. (2017);
Hirnschall et al. (2013); Yamazaki et al. (2014); Hirnschall et al. (2017).
AS-OCT image can provide high-resolution visualization of the crystalline lens
in vivo in the eyes of people in real-time without impacting the tissue, which
can help ophthalmologists get different information of the crystalline lens
through the circumferential scanning mode. Recent works have suggested that
the AS-OCT images can be used to locate the lens region and accurately
characterize opacities of different cataract types quantitatively Grulkowski
et al. (2018); Pawliczek et al. (2020). Fig. 5 offers an AS-OCT image, which
can quickly help us know the crystalline lens structure.

Figure 5: AS-OCT image. nuclear region is used for NC diagnosis; cortical
region is used for CC diagnosis; posterior subcapsular region is used for PSC
diagnosis.

Discussion: Though six different ophthalmic images are used for cataract
diagnosis, slit lamp images and fundus images are the most commonly-used
ophthalmic images for clinical cataract diagnosis and scientific research
purposes. This is because existing cataract classification/grading systems are
built on them. Slit lamp images can capture the lens region but cannot
distinguish the boundaries between nuclear, cortical, and capsular regions.
Hence, it is difficult for clinicians to diagnose different cataract types
accurately based on slit lamp images. Fundus images only contain opacity
information of cataract and do not contain location information of cataract,
which is mainly applied to cataract screening.

Retroillumination images are usually used to diagnose CC and PSC clinically,
which have not been widely studied. Digital camera images are ideal ophthalmic
images for cataract screening because they can be collected through mobile
phones, which are easy and cheap for most people. Like fundus images, digital
camera images only have opacity information of cataract but do not contain
location information of different cataract types. Ultrasonic images can
capture the lens region and evaluate the hardness of the cataract lens, but
they cannot distinguish different sub-regions, e.g., cortex region. AS-OCT
image is a new ophthalmic image that can distinguish different sub-regions,
e.g., cortex and nucleus regions, significant for cataract surgery planning
and cataract diagnosis. However, there is no cataract classification/grading
system built on AS-OCTs; thus, it is urgent to develop a clinical cataract
classification/grading system based on AS-OCT images. Moreover, existing
automatic AS-OCT image-based cataract classification has been rarely studied.

##  3 Cataract classification/grading systems

To classify or grade the severity levels of cataract (lens opacities)
accurately and quantitatively, it is crucial and necessary to build
standard/gold cataract classification/grading systems for clinical practice
and scientific research purposes. This section briefly introduces six existing
cataract classification/grading systems.

###  3.1 Lens opacity classification system

Lens opacity classification system (LOCS) was first introduced in 1988, which
has developed from LOCS I to LOCS III Chylack et al. (1993, 1988, 1989). LOCS
III is widely used for clinical diagnosis and scientific research. In the LOCS
III, as shown in Fig. 6, six representative slit lamp images for nuclear
cataract grading based on nuclear color and nuclear opalescence; five
representative retroillumination images for cortical cataract grading; five
representative retroillumination images for grading posterior subcapsular
cataract. The cataract severity level is graded on a decimal scale by spacing
intervals regularly.

Figure 6: Lens opacity classification system III.

###  3.2 Wisconsin grading System

Wisconsin grading system was proposed by the Wisconsin Survey Research
Laboratory in 1990 Klein et al. (1990); Group et al. (2001); Fan et al.
(2003). It contains four standard photographs for grading cataract severity
levels. The grades for cataract severity levels are as follows: grade 1, as
clear or clearer than Standard 1; grade 2, not as clear as Standard 1 but as
clear or clearer than Standard 2; grade 3, not as clear as Standard 2 but as
clear or clearer than Standard 3; grade 4, not as clear as Standard 3 but as
clear or clearer than Standard 4; grade 5, at least as severe as Standard 4;
and grade 6, 7 and 8, cannot grade due to severe opacities of the lens (please
see detail introduction of Wisconsin grading system in literature Klein et al.
(1990)). Wisconsin grading system also uses a decimal grade for cataract
grading with 0.1-unit interval space, and the range of the decimal grade is
from 0.1 to 4.9.

###  3.3 Oxford clinical cataract classification and grading system

Oxford Clinical Cataract Classification and Grading System (OCCCGS) is also a
slit-lamp image-based cataract grading system Sparrow et al. (1986); Hall et
al. (1997). Different to the LOCS III uses photographic transparencies of the
lens as cataract grading standards, it adopts standard diagrams and Munsell
color samples to grade the severity of cortical, posterior subcapsular, and
nuclear cataract Hall et al. (1997). In the OCCCGS, five standard grading
levels are used for evaluating the severity level of cataract based on
cataract features, such as cortical features, nuclear features, morphological
features, etc. Sparrow et al. (1986). E.g., the severity levels of nuclear
cataract are graded as follows: Grade 0: No yellow detectable; Grade 1: yellow
just detectable; Grade 2: definate yellow; Grade 3: orange yellow; Grade 4:
reddish brown; Grade 5: blackish brown Sparrow et al. (1986).

###  3.4 Johns Hopkins system

Johns Hopkins system (JHS) was first proposed in 1980s West et al. (1988). It
has four standard silt lamp images, which denotes the severity level of
cataract based on the opalescence of the lens. For nuclear cataract, Grade 1:
opacities that are definitely present but not thought to reduce visual acuity;
Grade 2: opacities are consistent with visual acuity between 20/20 and 20/30;
Grade 3 opacities are consistent with vision between 20/40 and 20/100; Grade
4: opacities are consistent with the vision of 20/200 or less.

###  3.5 WHO cataract grading system

WHO cataract grading system was developed by a group of experts in WHO
Thylefors et al. (2002); for the Prevention of Blindness and Group (2001). The
target to develop it is to enable relatively inexperienced observers to grade
the most common types of cataracts reliably and efficiently. It uses four
severity levels for grading NC, CC, and PSC based on four standard images
accordingly.

###  3.6 Fundus image-based cataract classification system

Xu et al. Xu et al. (2010) proposed a fundus image-based cataract
classification system (FCS) through observing the blur level. They used five
levels to evaluate the blur levels on fundus images: grade 0: clear; grade 1:
the small vessel region was blurred; grade 2: the larger branches of retinal
vein or artery were blurred; grade 3: the optic disc region was blurred; grade
4: The whole fundus image was blurred.

Discussion: From the above-mentioned six existing cataract
classification/grading systems, we can conclude that five cataract
classification systems are built on slit lamp images, and one is built on
fundus images, which can explain that most existing cataract works based on
these two ophthalmic imaging modalities. However, these cataract
classification/grading systems are subjective due to the limitations of these
two imaging devices. Furthermore, to improve the precision of cataract
diagnosis and the efficiency of cataract surgery, it is necessary to develop
new and objective cataract classification systems on other ophthalmic image
modalities, e.g., AS-OCT images.

##  4 Datasets

In this section, we introduce ophthalmic image datasets used for cataract
classification/grading, which can be grouped in private datasets and public
datasets.

###  4.1 Private datasets

ACHIKO-NC dataset Liu et al. (2013): ACHIKO-NC is the slit-lamp lens images
dataset selected from the SiMES I database, used to grade nuclear cataracts.
It comprised 5378 images with decimal grading scores (0.3 to 5.0).
Professional clinicians determine the grading score of each slit lamp image.
ACHIKO-NC is a widely used dataset for automatic nuclear cataract grading
according to existing works.

ACHIKO-Retro dataset Liu et al. (2013): ACHIKO-Retro is the retro-illumination
lens image dataset selected from SiMES I database, used to grade CC and PSC.
Each lens has two eye image types: anterior image and posterior image. The
anterior image focuses on the plane centered in the anterior cortex region,
and the posterior image focuses on the posterior capsule region. Most previous
CC and PSC grading works were conducted on the ACHIKO-Retro dataset.

CC-Cruiser dataset Jiang et al. (2021a): CC-Cruiser is the slit lamp image
dataset collected from Zhongshan Ophthalmic Center (ZOC) of Sun Yat-Sen
University, which is used for cataract screening. It is comprised of 476
normal images and 410 infantile cataract images.

Multicenter dataset Jiang et al. (2021a): Multicenter is the slit lamp image
dataset, which is comprised of 336 normal images and 421 infantile cataract
images. It was collected from four clinical institutions: the Central Hospital
of Wuhan, Shenzhen Eye Hospital, Kaifeng Eye Hospital, and the Second
Affiliated Hospital of Fujian Medical University.

###  4.2 Public datasets

EyePACS dataset Cuadros and Bresnick (2009): EyePACS is the fundus image
dataset collected from EyePACS, LLC , a free platform for retinopathy
screening, used to classify different levels of cataract. It is made available
by California Healthcare Foundation. The dataset comprises 88,702 fundus
retinal images in which 1000 non-cataract images and 1441 cataract images are
provided.

HRF dataset Pratap and Kokil (2019): The high-resolution fundus (HRF) image
database is selected from different open-access datasets: structured analysis
of the retina (STARE) Hoover et al. (2000), standard diabetic retinopathy
database (DIARETDB0) Kauppi et al. (2006), e-ophtha Decenciere et al. (2013),
methods to evaluate segmentation and indexing techniques in the field of
retinal ophthalmology (MESSIDOR) database Decencière et al. (2014), digital
retinal images for vessel extraction (DRIVE) database Staal et al. (2004),
fundus image registration (FIRE) Hernandez-Matas et al. (2017) dataset,
digital retinal images for optic nerve segmentation database (DRIONS-DB)
Carmona et al. (2008), Indian diabetic retinopathy image dataset (IDRiD)
Porwal et al. (2018), available datasets released by Dr. Hossein Rabbani
Mahmudi et al. (2014), and other Internet sources.

##  5 Machine learning techniques

This section mainly investigates on ML techniques for cataract
classification/grading over the years, which is comprised of conventional ML
methods and deep learning methods.

Table 1: Conventional ML methods for cataract classification/grading based on ophthalmic images. | Literature | Method | Image Type | Year | Application | Cataract Type
---|---|---|---|---|---|---
| Li et al. (2009) | ASM + SVR | Slit Lamp Image | 2009 | Grading | NC
| Huang et al. (2009b) | ASM + Ranking | Slit Lamp Image | 2009 | Grading | NC
| Li et al. (2010a) | ASM + SVR | Slit Lamp Image | 2010 | Grading | NC
| Huang et al. (2010) | ASM + Ranking | Slit Lamp Image | 2010 | Grading | NC
| Li et al. (2007) | ASM + LR | Slit Lamp Image | 2007 | Grading | NC
| Xu et al. (2016) | SF + SWLR | Slit Lamp Image | 2016 | Grading | NC
| Xu et al. (2013) | BOF + GSR | Slit Lamp Image | 2013 | Grading | NC
| Caixinha et al. (2016) | SVM | Slit Lamp Image | 2016 | Grading | NC
| Jiang et al. (2017) | SVM | Slit Lamp Image | 2017 | Grading | NC
| Wang et al. (2017) | SVM | Slit Lamp Image | 2017 | Grading | NC
| Srivastava et al. (2014) | IGB | Slit Lamp Image | 2014 | Grading | NC
| Jiang et al. (2017) | RF | Slit Lamp Image | 2017 | Classification | NC
| Cheng (2018) | SRCL | Slit Lamp Image | 2018 | Grading | NC
| Jagadale et al. (2019) | Hough Circular Transform | Slit Lamp Image | 2019 | Classification | NC
| Kai et al. (2019) | RF | Slit Lamp Image | 2019 | Classification | PC
| Kai et al. (2019) | NB | Slit Lamp Image | 2019 | Classification | PC
| Li et al. (2010b) | Canny + Spatial Filter | Retroillumination Image | 2010 | Classification | PSC
| Li et al. (2008b) | EF + PCT | Retroillumination Image | 2008 | Classification | PSC
| Chow et al. (2011) | Canny | Retroillumination Image | 2011 | Classification | PSC
| Zhang and Li (2017) | MRF | Retroillumination Image | 2017 | Classification | PSC
| Gao et al. (2011) | LDA | Retroillumination Image | 2011 | Classification | PSC & CC
| Li et al. (2008a) | Radial-edge & Region-growing | Retroillumination Image | 2008 | Classification | CC
| Caxinha et al. (2015) | PCA | Ultrasonic Image | 2015 | Classification | Cataract
| Caxinha et al. (2015) | Bayes | Ultrasonic Image | 2015 | Classification | Cataract
| Caxinha et al. (2015) | KNN | Ultrasonic Image | 2015 | Classification | Cataract
| Caxinha et al. (2015) | SVM | Ultrasonic Image | 2015 | Classification | Cataract
| Caxinha et al. (2015) | FLD | Ultrasonic Image | 2015 | Classification | Cataract
| Caixinha et al. (2016) | SVM | Ultrasonic Image | 2016 | Classification | Cataract
| Caixinha et al. (2016) | RF | Ultrasonic Image | 2016 | Classification | Cataract
| Caixinha et al. (2016) | Bayes Network | Ultrasonic Image | 2016 | Classification | Cataract
| Caixinha et al. (2014b) | PCA + SVM | Ultrasonic Image | 2014 | Classification | Cataract
| Caixinha et al. (2014a) | Nakagami Distribution +CRT | Ultrasonic Image | 2014 | Classification | Cataract
| Jesus et al. (2013) | SVM | Ultrasonic Image | 2013 | Classification | Cataract
| Fuadah et al. (2015) | GLCM + KNN | Digital Camera Image | 2015 | Classification | Cataract
| Fuadah et al. (2015) | GLCM + KNN | Digital Camera Image | 2015 | Classification | Cataract
| Pathak and Kumar (2016) | K-Means | Digital Camera Image | 2016 | Classification | Cataract
| Patwari et al. (2011) | IMF | Digital Camera Image | 2016 | Classification | Cataract
| Khan et al. (2018) | SVM | Digital Camera Image | 2016 | Classification | Cataract
| Guo et al. (2015) | WT + MDA | Fundus Image | 2015 | Classification | Cataract
| Yang et al. (2016) | Wavelet-SVM | Fundus Image | 2015 | Classification | Cataract
| Yang et al. (2016) | Texture-SVM | Fundus Image | 2015 | Classification | Cataract
| Yang et al. (2016) | Stacking | Fundus Image | 2015 | Classification | Cataract
| Fan et al. (2015) | PCA + Bagging | Fundus Image | 2015 | Classification | Cataract
| Fan et al. (2015) | PCA + RF | Fundus Image | 2015 | Classification | Cataract
| Zhang et al. (2019a) | Multi-feature Fusion&Stacking | Fundus Image | 2019 | Classification | Cataract
| Fan et al. (2015) | PCA + GBDT | Fundus Image | 2015 | Classification | Cataract
| Fan et al. (2015) | PCA + SVM | Fundus Image | 2015 | Classification | Cataract
| Cao et al. (2020b) | Haar Wavelet + Voting | Fundus Image | 2019 | Classification | Cataract
| Qiao et al. (2017) | SVM + GA | Fundus Image | 2017 | Classification | Cataract
| Huo et al. (2019) | AWM + SVM | Fundus Image | 2019 | Classification | Cataract
| Song et al. (2016) | DT | Fundus Image | 2016 | Classification | Cataract
| Song et al. (2016) | Bayesian Network | Fundus Image | 2016 | Classification | Cataract
| Song et al. (2019) | DWT+SVM | Fundus Image | 2019 | Classification | Cataract
| Song et al. (2019) | SSL | Fundus Image | 2019 | Classification | Cataract
| Zhang et al. (2022a) | RF | AS-OCT image | 2021 | Classification | NC
| Zhang et al. (2022a) | SVM | AS-OCT image | 2021 | Classification | NC

###  5.1 Conventional machine learning methods

Over the past years, scholars have developed massive state-of-the-art
conventional ML methods to automatically classify/grade cataract severity
levels, aiming to assist clinicians in diagnosing cataract efficiently and
accurately. These methods consist of feature extraction and classification
/grading, as shown in Fig. 7. Table 1 summarizes conventional ML methods for
cataract classification/grading based on different ophthalmic images.

Figure 7: Flowchart of conventional machine learning based classification and
grading.

####  5.1.1 Feature extraction

Considering the characteristics of different imaging techniques and cataract
types, we introduce feature extraction methods based on ophthalmic image
modalities.

Slit lamp image: The procedures to extract features from slit lamp images are
comprised of the lens structure detection and feature extraction.

Figure 8: The procedures to extract image features from slit lamp images for
automatic nuclear cataract classification/grading.

Fig. 8 offers a representative slit lamp image-based feature extraction
flowchart. Firstly, according to the histogram analysis results of the lens,
the foreground of the lens is detected by setting the pixel thresholding, and
the background of the lens is even based on slit lamp images. Afterward, we
analyze the profile on the horizontal median line of the image. The largest
cluster on the line is detected as the lens, and the centroid of the cluster
is detected as the horizontal coordinate of the lens center. Then, we get the
profile on the vertical line through the point. Finally, the center of the
lens is estimated, and the lens can be further estimated as an ellipse with
the semi-major axis radius estimated from the horizontal and vertical profile.

The lens contour or shape needs to be captured by following the lens location.
Researchers commonly used the active shape model (ASM) method for the lens
contour detection Li et al. (2010a, b, 2008b) and achieved 95% accuracy of the
lens structure detection. The ASM can describe the object shape through an
iterative refinement procedure to fit an example of the object into a new
image based on the statistical models Li et al. (2007). Based on the detected
lens contour, many feature extraction methods have been proposed to extract
informative features, such as bag-of-features (BOF) method, grading protocol-
based method, semantic reconstruction-based method, statistical texture
analysis Huang et al. (2009b); Xu et al. (2016); Li et al. (2010b); Xu et al.
(2013); Gao et al. (2011); Srivastava et al. (2014), etc.

Retroillumination image: It also consists of two stages for feature extraction
based on retroillumination images: pupil detection and opacity detection Li et
al. (2008b, 2010b); Chow et al. (2011); Gao et al. (2011), as shown in Fig. 9.
Researchers usually use a combination of the Canny edge detection method, the
Laplacian method, and the convex hull method to detect the edge pixel in the
pupil detection stage. The non-linear least-square fitting method is used to
fit an ellipse based on the detected pixels. In the opacity detection stage,
the input image is transformed into the polar coordinate at first. Based on
the polar coordinate, classical image processing methods are applied to detect
opacity such as global threshold, local threshold, edge detection, and region
growing. Apart from the above methods, literature Zhang and Li (2017) uses
Watershed and Markov random fields (MRF) to detect the lens opacity, and
results showed that the proposed framework got competitive results of PSC
detection.

Figure 9: The procedures to extract features from retroillumination images for
cortical cataract (CC) and posterior subcapsular cataract (PSC) diagnosis.

Ultrasound Images & Digital Camera Images & AS-OCT Images: For ultrasound
images, researchers adopt the Fourier Transform (FT) method, textural analysis
method, and probability density to extract features Caixinha et al. (2016,
2014b, 2014a).

The procedures to extract features from digital camera images is the same to
slit lamp images, but different image processing methods are used, such as
Gabor Wavelet transform, Gray level Co-occurrence Matrix (GLCM), morphological
image feature, and Gaussian filter Patwari et al. (2011); Fuadah et al.
(2015); Pathak and Kumar (2016); Fuadah et al. (2015).

The steps to detect lens region for AS-OCT images are also similar to slit
lamp images. Literature Zhang et al. (2022a) uses intensity-based statistics
method and intensity histogram method to extract image features from AS-OCT
images.

Fundus image: Over the years, researchers have developed various wavelet
transform methods to preprocess fundus images for extracting valuable
features, as shown in Fig. 10, such as discrete wavelet transform (DWT),
discrete cosine transform (DCT), Haar wavelet transform, and top-bottom hat
transformation Zhou et al. (2019); Guo et al. (2015); Cao et al. (2020b).

Figure 10: The procedures to extract features from fundus images for cataract
classification or screening.

####  5.1.2 Classification & grading

In this section, we mainly introduce conventional machine learning methods for
cataract classification/grading.

Support vector machine:  Support vector machine (SVM) is a classical
supervised machine learning technique, which has been widely be used for
classification and linear regression tasks. It is a popular and efficient
learning method for medical imaging applications. For the cataract grading
task, Li et al. Li et al. (2009, 2010a) utilized support vector machine
regression (SVR) to grade the severity level of cataract and achieved good
grading results based on slit lamp images. The SVM classifier is widely used
in different ophthalmic images for the cataract classification task. E.g.,
literature Jiang et al. (2017); Wang et al. (2017) applies SVM to classify
cataract severity levels based on slit-lamp images. For other ophthalmic image
types, SVM also achieves good results based on extracted features Khan et al.
(2018); Yang et al. (2016, 2016); Qiao et al. (2017).

Linear regression: Linear regression (LR) is one of the most well-known ML
methods and has been used to address different learning tasks. The concept of
LR is still a basis for other advanced techniques, like deep neural networks.
Linear functions determine its model in LR, whose parameters are learned from
data by training. Literature Li et al. (2007) first studies automatic cataract
grading with LR on slit lamp images and achieves good grading results.
Followed by literature Li et al. (2007), Xu et al. Xu et al. (2013, 2016)
proposed the group sparsity regression (GSR) and similarity weighted linear
reconstruction (SWLR) for cataract grading and achieved better grading
results.

K-nearest neighbors: K-nearest neighbors (KNN) method is a simple, easy-to-
implement supervised machine learning method used for classification and
regression tasks. It uses similarity measures to classify new cases based on
stored instances. Y.N. Fuadah et al.Fuadah et al. (2015) used the KNN to
detect cataract on digital camera images and achieved 97.2% accuracy.
Literature Caxinha et al. (2015) also uses KNN for cataract classification on
Ultrasonic images, which were collected from the animal model.

Ensemble learning method:  Ensemble learning method uses multiple machine
learning methods to solve the same problem and usually obtain better
classification performance. Researchers Yang et al. (2016); Zhang et al.
(2019a); Cao et al. (2020b) have used several ensemble learning methods for
cataract classification, such as Stacking, Bagging, and Voting. Ensemble
learning methods achieved better cataract grading results than single machine
learning methods.

Ranking: Ranking denotes a relationship within the list in a/an
descending/ascending order. Researchers Huang et al. (2009b, 2010) applied the
ranking strategy to automatic cataract grading by computing the score of each
image from the learned ranking function such as RankBoost and Ranking SVM and
achieved competitive performance.

Other machine learning methods: Apart from the above-mentioned conventional ML
methods, other advanced ML methods are also proposed for automatic cataract
classification/grading, such as Markov random field (MRF), random forest (RF),
Bayesian network, linear discriminant analysis (LDA), k-means, and decision
tree (DT), etc. E.g., literature Zhang and Li (2017) applies Markov random
field (MRF) to automatic CC classification and achieves good results. Song et
al. (2019) uses semi-supervised learning (SSL) framework for cataract
classification based on fundus images and achieves competitive performance.

Furthermore, we can draw the following conclusions:

  * •

For feature extraction, despite the characteristics of ophthalmic images,
various image processing techniques are developed to extract useful features,
like edge detection method, wavelet transform method, texture extraction
methods, etc. However, no previous works systematically compare these feature
extraction methods based on the same ophthalmic images, providing a standard
benchmark for other researchers. Furthermore, existing works have not verified
the effectiveness of a classical feature extraction method on different
ophthalmic images, which is significant for the generalization ability of a
feature extraction method and building commonly-used feature extraction
baselines on cataract classification/grading tasks.

  * •

For classification/grading, researchers have made great efforts in developing
state-of-the-art ML methods in recognizing cataract severity levels on
ophthalmic images and demonstrated that ML methods can achieve competitive
performance on extracted features. We found that no existing research has made
a comparison between ML methods comprehensively based on the same ophthalmic
image or different ophthalmic image types; thus, it is necessary to build
conventional ML baselines for cataract classification/grading, which can help
researchers reproduce previous works and prompt the development of cataract
classification/grading tasks.

###  5.2 Deep learning methods

In recent years, with the rapid development of deep learning techniques, many
deep learning methods ranging from the artificial neural network (ANN),
multilayer perceptron (MLP) neural network, backpropagation neural network
(BPNN), convolutional neural network (CNN), recurrent neural network (RNN),
attention mechanism, to Transformer-based methods, which have been applied to
solve different learning tasks such as image classification and medical image
segmentation. In this survey, we mainly pay attention to deep learning methods
in cataract classification tasks, and Table 2 provides a summary of deep
learning methods for cataract classification/ grading based on different
ophthalmic images.

Table 2: Deep learning methods for cataract classification/grading on different ophthalmic images. | Literature | Method | Image Type | Year | Application | Cataract Type
---|---|---|---|---|---|---
| Gao et al. (2015) | CNN+RNN | Slit Lamp Image | 2015 | Grading | NC
| Liu et al. (2017a) | CNN | Slit Lamp Image | 2017 | Classification | PCO
| Jiang et al. (2017) | CNN | Slit Lamp Image | 2017 | Classification | PCO
| Long et al. (2017) | CNN | Slit Lamp Image | 2017 | Classification | Cataract
| Zhang et al. (2018a) | CNN | Slit Lamp Image | 2018 | Classification | Cataract
| Jiang et al. (2018) | CNN+RNN | Slit Lamp Image | 2018 | Classification | PCO
| Xu et al. (2019a) | Faster R-CNN | Slit Lamp Image | 2019 | Grading | NC
| Ting et al. (2019) | CNN | Slit Lamp Image | 2019 | Classification | NC
| Jun et al. (2019) | CNN | Slit Lamp Image | 2019 | Classification | NC
| Wu et al. (2019) | CNN | Slit Lamp Image | 2019 | Classification | NC
| Hu et al. (2020) | CNN | Slit Lamp Image | 2020 | Classification | NC
| Jiang et al. (2021b) | CNN | Slit Lamp Image | 2021 | Classification | Cataract
| Jiang et al. (2021a) | R-CNN | slit lamp Image | 2021 | Classification | PCO
| Hu et al. (2021) | CNN | slit lamp Image | 2021 | Classification | cataract
| Tawfik et al. (2018) | Wavelet + ANN | Digital Camera Image | 2016 | Classification | Cataract
| Caixinha et al. (2016) | MLP | Ultrasonic Image | 2016 | Classification | Cataract
| Zhang et al. (2020a) | Attention | Ultrasonic Image | 2020 | Classification | Cataract
| Wu et al. (2021) | CNN | Ultrasonic Image | 2021 | Classification | Cataract
| Yang et al. (2016) | BPNN | Fundus Image | 2015 | Classification | Cataract
| Linglin Zhang et al. (2017) | CNN | Fundus Image | 2017 | Classification | Cataract
| Dong et al. (2017) | CNN | Fundus Image | 2017 | Classification | Cataract
| Zhang et al. (2019a) | CNN | Fundus Image | 2019 | Classification | Cataract
| Zhou et al. (2019) | MLP | Fundus Image | 2019 | Classification | Cataract
| Zhou et al. (2019) | CNN | Fundus Image | 2019 | Classification | Cataract
| Zhang et al. (2017) | CNN | Fundus Image | 2019 | Classification | Cataract
| Pratap and Kokil (2019) | CNN | Fundus Image | 2019 | Classification | Cataract
| Xu et al. (2019b) | CNN | Fundus Image | 2020 | Classification | Cataract
| Pratap and Kokil (2021) | CNN | Fundus Image | 2021 | Classification | Cataract
| Imran et al. (2021) | CNN+RNN | Fundus Image | 2021 | Classification | Cataract
| Junayed et al. (2021) | CNN | Fundus Image | 2021 | Classification | Cataract
| Tham et al. (2022) | CNN | Fundus Image | 2022 | Classification | Cataract
| Zhang et al. (2020b) | CNN | AS-OCT Image | 2020 | Classification | NC
| Xiao et al. (2021a) | CNN | AS-OCT Image | 2021 | Classification | NC
| Xiao et al. (2021b) | Attention | AS-OCT Image | 2021 | Classification | NC
| Zhang et al. (2022c) | Attention | AS-OCT Image | 2022 | Classification | NC
| Zhang et al. (2022b) | Attention | AS-OCT Image | 2022 | Classification | NC

####  5.2.1 Multilayer perceptron neural networks

Multilayer perceptron (MLP) neural network is one type of artificial neural
network (ANNs) composed of multiple hidden layers. Researchers often combined
the MLP with hand-engineered feature extraction methods for cataract
classification to get expected performance. Zhou et al. Zhou et al. (2019)
combined a shallow MLP model with feature extraction methods to classify
cataract severity levels automatically. Caixinha et al. Caixinha et al.
(2014a) utilized the MLP model for the cataract classification and achieved
96.7% of accuracy.

Deep neural networks (DNNs) comprise many hidden layers, and they are capable
of capturing more informative feature representations from ophthalmic images
through comparisons to MLP. DNN usually is used as dense (fully-connected)
layers of CNN models. Literature Zhang et al. (2017); Dong et al. (2017);
Zhang et al. (2019a) uses DNN models for cataract classification and achieves
accuracy over 90% on fundus images.

Recently, several works Tolstikhin et al. (2021); Lian et al. (2021); Mansour
et al. (2021); Touvron et al. (2021); Zheng et al. (2022) have demonstrated
that constructing deep network architectures purely on multi-layer perceptrons
(MLPs) can get competitive performance on ImageNet classification task with
spatial and channel-wise token mixing, to aggregate the spatial information
and build dependencies among visual features. These MLP-based models have
promising results in classical computer vision tasks like image
classification, semantic segmentation, and image reconstruction. However, to
the best of our knowledge, MLP-based models have not been used to tackle
ocular diseases tasks include cataract based on different ophthalmic images,
which can be an emerging research direction for cataract
classification/grading in the future.

####  5.2.2 Convolutional neural networks

Convolutional neural network (CNN) has been widely used in the ophthalmic
image processing field and achieved surprisingly promising performance Szegedy
et al. (2015); Krizhevsky et al. (2012); Chen et al. (2015); Fu et al. (2016);
Abràmoff et al. (2016); Litjens et al. (2017); Mansoor et al. (2016); Xie et
al. (2017); Szegedy et al. (2016); Huang et al. (2017); Zhang et al. (2018b).
CNN consists of an input Layer, multiple convolutional layers, multiple
pooling layers, multiple fully-connected layers, and an output layer. The
function of convolutional layers is to learn low-, middle, and high-level
feature representations from the input images through massive convolution
operations in different stages.

For slit lamp images, previous works usually used classical image processing
methods to localize the region of interest (ROI) of the lens, and the lens ROI
is used as the inputs features of CNN models Liu et al. (2017a); Grewal et al.
(2018); Xu et al. (2019a). E.g., Liu et al.Liu et al. (2017a) proposed a CNN
model to detect and grade the severe levels of posterior capsular
opacification (PCO), which used the lens ROI as the inputs of CNN. Literature
Xu et al. (2019a) uses original images as inputs for CNN models to detect
cataract automatically through Faster R-CNN. Literature Long et al. (2017)
develops an artificial intelligence platform for congenital cataract diagnosis
and obtains good diagnosis results. Literature Wu et al. (2019) proposes a
universal artificial intelligence platform for nuclear cataract management and
achieves good results.

For fundus images, literature Xu et al. (2019b); Zhang et al. (2017); Dong et
al. (2017); Zhang et al. (2019a) achieves competitive classification results
with deep CNNs. Zhou et al.Zhou et al. (2019) proposed the EDST-ResNet model
for cataract classification based on fundus images where they used discrete
state transition function Deng et al. (2018) as the activation function, to
improve the interpretability of CNN models. Fig. 12 provides a representative
CNN framework for cataract classification on fundus images, which can help
audiences know this task easily.

For AS-OCT images, literature Yin et al. (2018); Zhang et al. (2019b); Cao et
al. (2020a) proposes CNN-based segmentation frameworks for automatic lens
region segmentation based on AS-OCT images, which can help ophthalmologists
localize and diagnose different types of cataract efficiently. Zhang et al.
Zhang et al. (2020b) proposed a novel CNN model named GraNet for nuclear
cataract classification on AS-OCT images but achieved poor results. Xiao et
al. (2021a) uses a 3D ResNet architecture for cataract screening based on 3D
AS-OCT images. Zhang et al. Zhang et al. (2022b) tested the NC classification
performance of state-of-the-art CNNs like ResNet, VGG, and GoogleNet on AS-OCT
images, and the results showed that EfficientNet achieved the best
performance.

Figure 11:  An example of a convolutional neural network (CNN) model for
cataract classification on fundus image Xu et al. (2019b).

####  5.2.3 Recurrent neural networks

Recurrent neural network (RNN) is a typical feedforward neural network
architecture where connections between nodes form a directed or undirected
graph along a temporal sequence. RNN are skilled at processing sequential data
effectively for various learning tasks Hu et al. (2020), e.g., speech
recognition. Over the past decades, many RNN variants have been developed to
address different tasks, where Long short-term memory (LSTM) network is the
most representative one. However, researchers have not used pure RNN
architecture to classify cataract severity levels yet, which can be a research
direction for automatic cataract classification.

####  5.2.4 Attention mechanisms

Over the past years, attention mechanisms have been proven useful in various
fields, such as computer vision Choi et al. (2020), natural language
processing (NLP) Vaswani et al. (2017), and medical data processing Zhang et
al. (2019c). Generally, attention mechanism can be taken an adaptive weighting
process in a local-global manner according to feature representations of
feature maps. In computer vision, attention can be classified into five
categories: channel, spatial, temporal attention, branch attention, and
attention combinations such as channel & spatial attention Guo et al. (2021).
Each attention category has a different influence on the computer vision
field. Researchers have recently used attention-based CNN models for cataract
classification on different ophthalmic images. Zhang et al. Zhang et al.
(2020a) proposed a residual attention-based network for cataract detection on
Ultrasound Images. Xiao et al. Xiao et al. (2021b) applied a gated attention
network (GCA-Net) to classify nuclear cataract severity levels on AS-OCT
images and got good performance. Zhang et al. (2022b) presents a mixed pyramid
attention network for AS-OCT image-based nuclear cataract classification in
which they construct the mixed pyramid attention (MPA) block by considering
the relative significance of local-global feature representations and
different feature representation types, as shown in Fig. 12.

Especially, self-attention is one representative kind of attention mechanism.
Due to its effectiveness in capturing capture long-range dependencies and
generality, it has been playing an increasingly important role in a variety of
learning tasks Vaswani et al. (2017); Wang et al. (2018); Yu et al. (2021).
Massive deep self-attention networks (e.g., Transformer) have achieved state-
of-the-art performance through mainstream CNNs on visual tasks. Vision
Transformer (ViT) Dosovitskiy et al. (2020) is a first-pure transformer
architecture proposed for image classification and gets promising performance.
Recently, researchers also extended the transformer-based models for different
medical image analysis tasks He et al. (2022); however, no current research
work has utilized transformer-based architectures to recognize cataract
severity levels.

Figure 12:  An example of attention-based CNN architecture for cataract
classification on AS-OCT images.

####  5.2.5 Hybrid neural networks

TA hybrid neural network indicates that a neural network is comprised of two
or more two deep neural network types. In recent years, researchers have
increasingly used hybrid neural networks to address different learning tasks
Gulati et al. (2020). Due to its ability in inheriting the advantages from
different neural network architectures, such as CNN, MLP, and transformer.
Literature Jiang et al. (2018); Gao et al. (2015); Imran et al. (2021)
proposes the hybrid neural network models for cataract classification /grading
by considering the characteristics of RNN and CNN models, respectively. In the
future, we believe that more advanced hybrid neural network models will be
designed for cataract classification/grading based on different ophthalmic
image modalities.

Figure 13: A hybrid neural network model for nuclear cataract grading on slit
lamp images, comprised of a CNN model and a RNN model Gao et al. (2015).

Discussion: From Table 1 and Table 2, we can conclude as follows:

  * •

Ophthalmic image perspective: Slit lamp images and fundus images account for
most automatic cataract classification/grading works, this is because these
two ophthalmic image modalities are easy to access and have the clinical gold
standards. Except for ultrasound images collected from the animal model and
human subjects, five other ophthalmic imaging modalities were used for
automatic cataract classification/grading.

  * •

Cataract classification/grading perspective: Existing works mainly focused on
cataract screening, and most of them achieved over 80% accuracy. The number of
cataract classification works is more than the number of cataract grading
works. Since discrete labels are easy to access and be confirmed through
comparisons continuous labels.

  * •

Publication year perspective: Conventional machine learning methods were first
used to classify or grade cataract automatically. With the emergence of deep
learning, researchers have gradually constructed advanced deep neural network
architectures to automatically predict cataract severity levels due to their
surprising performance. However, the interpretability of deep learning methods
is worse than conventional machine learning methods.

  * •

Learning paradigm perspective: Most previous machine learning methods belong
to supervised learning methods, and only two existing methods used semi-
supervised learning methods. Specifically, unsupervised and semi-supervised
learning methods have achieved competitive performance in computer vision and
NLP, which have not been widely applied to automatic cataract diagnosis.

  * •

Deep neural network architecture perspective: According to Table 2, we can see
that CNNs account for over 50% deep neural network architectures. Two reasons
can explain that: 1) Ophthalmic images are the most commonly-used way for
cataract diagnosis by clinicians. 2) Compared with RNN and MLP, CNN are
skilled at processing image data. To enhance precision of cataract diagnosis,
it is better to combine image data with non-imaging data, e.g., age.

  * •

Performance comparison on private/public datasets: Pratap et al. Pratap and
Kokil (2021) made a comparison of AlexNet, GoogleNet, ResNet, ResNet50, and
SqueezeNet for the cataract classification on the public EyePACS dataset, and
results showed all CNN models obtained over 86% in the accuracy, and AlexNet
got the best performance. Zhang et al. (2022b) offers the NC classification
results of attention-based CNNs (CBAM, ECA, GCA, SPA, and SE on the private NC
dataset, and results showed SE obtained better performance than other strong
attention methods. Furthermore, Xu et al. (2019b, a); Pratap and Kokil (2021,
2019) used pre-trained CNNs for automatic cataract classification on the
EyePACS dataset according to transfer learning strategy, and they also
concluded that training pre-trained CNNs benefited the cataract classification
performance than training CNNs from scratch.

  * •

Data augmentation techniques: Researchers use the commonly-used method for
cataract classification/grading to augment ophthalmic image data for deep
neural networks, such as flipping, cropping, rotation, and noise injection. To
further validate or enhance the generalization ability of deep neural network
models on cataract classification/grading tasks, other data augmentation
techniques should be considered: translation, color space transformations,
random erasing, adversarial training, meta-learning, etc Shorten and
Khoshgoftaar (2019).

##  6 Evaluation measures

This section introduces evaluation measures to assess the performance of
cataract classification/grading. In this survey, Classification denotes the
cataract labels used for learning are discrete, e.g., 1,2,3,4, while grading
denotes cataract labels are continuous, such as 0.1,0.5,1.0, 1.6, and 3.3.

For cataract classification, accuracy (ACC), sensitivity (recall),
specificity, precision, F1-measure (F1), and G-mean are commonly used to
evaluate the classification performance. Guo et al. (2015); Cao et al.
(2020b); Jiang et al. (2018).

$ACC=\frac{TP+TN}{TP+TN+FP+FN},$ (1) $Sensitivity=\frac{TP}{TP+FN},$ (2)

$Specificity=\frac{TN}{TN+FP},$ (3)

$Precision=\frac{TP}{TP+FP},$ (4)

$F1=\frac{2*precision*recall}{precision+recall},$ (5)

$G-mean=\sqrt{\frac{TP}{TP+FN}*\frac{TN}{TN+FP}},$ (6)

where TP, FP, TN, and FN denote the numbers of true positives, false
positives, true negatives, and false negatives, respectively. Other evaluation
measures like receiver operating characteristic curve (ROC), the area under
the ROC curve (AUC), and kappa coefficient Cao et al. (2020b) are also applied
to measure the overall performance.

For cataract grading, the following evaluation measures are used to evaluate
the overall performance, titled the exact integral agreement ratio $R_{0}$,
the percentage of decimal grading errors $\leqslant 0.5$ $R_{e0.5}$, the
percentage of decimal grading errors $\leqslant 1.0$ $R_{e1.0}$, and the mean
absolute error $\varepsilon$ Li et al. (2007); Cheung et al. (2011); Xu et al.
(2016); Huiqi Li et al. (2008); Huang et al. (2010).

$R_{0}=\frac{\lvert\left\lceil G_{gt}\right\rceil=\left\lceil
G_{pr}\right\rceil\rvert_{0}}{N},$ (7)

$R_{e0.5}=\frac{\lvert\lvert G_{gt}-G_{pr}\rvert\leqslant 0.5\rvert_{0}}{N},$
(8)

$R_{e1.0}=\frac{\lvert\lvert G_{gt}-G_{pr}\rvert\leqslant 1.0\rvert_{0}}{N},$
(9)

$\varepsilon=\frac{\sum\lvert G_{gt}-G_{pr}\rvert}{N},$ (10)

where $G_{gt}$ and $G_{pr}$ denote the ground-truth grade and the predicted
grade. $\left\lceil.\right\rceil$ is the ceiling function, $\lvert.\rvert$ is
the absolute function, $\lvert.\rvert_{0}$ a function that counts the number
of non-zero values, and N denotes the number of images.

##  7 Challenges and possible solutions

Although researchers have made significant development in automatic cataract
classification/grading over the years, this field still has challenges. This
section presents these challenges and gives possible solutions.

###  7.1 Lacking public cataract datasets

PPublic cataract datasets are a very critical issue for cataract
classification/grading. Previous works have made tremendous progress in
automatic cataract/grading Xu et al. (2013); Foong et al. (2007); Caixinha et
al. (2014b); Guo et al. (2015), there is no public and standard ophthalmology
image dataset available except for public challenges datasets and multiple
ocular diseases. Hence, it is difficult for researchers to follow previous
works because the cataract dataset is unavailable. To this problem, it is
necessary and significant to build public and standard ophthalmology image
datasets based on standardized medical data collection and storage protocol.
Public cataract datasets can be collected from hospitals and clinics with
ophthalmologists’ help. This dataset collection mode can ensure the quality
and diversity of cataract data and help researchers develop more advanced ML
methods.

###  7.2 Developing standard cataract classification/grading protocols based
on new ophthalmic imaging modalities

Most existing works used the LOCS III as the clinical gold diagnosis standard
to grade/classify cataract severity levels for scientific research purposes
and clinical practice. However, the LOCS III is developed based on slit-lamp
images, which may not work well for ophthalmic images, such as fundus images,
AS-OCT images, and Ultrasonic images. To solve this problem, researchers have
made much effort in constructing new cataract classification/grading standards
for other ophthalmic image types. E.g., researchers refer to the WHO Cataract
Grading System and develop a cataract classification/grading protocol for
fundus images Xu et al. (2010) based on clinical research and practice, which
is widely used in automatic fundus-based cataract classification Xu et al.
(2019b); Zhang et al. (2017); Dong et al. (2017); Zhang et al. (2019a).

To address the issue of developing standard cataract classification/grading
protocols for new eye images, e.g., AS-OCT images. In this survey, we propose
two possible solutions for reference as follows.

  * •

Developing a cataract grading protocol based on the clinical verification.
Literature Grulkowski et al. (2018); Pawliczek et al. (2020) uses AS-OCT
images to observe the lens opacities of patients based on the LOCS III in
clinical practice, and statistical results showed that high correlation
between cataract severity levels and clinical features with inter-class and
intra-class analysis. The clinical finding may provide clinical support and
reference. Hence, it is likely to develop a referenced cataract
classification/grading protocol for ASOCT images based on the clinical
verification method like the LOCS III.

  * •

Building the mapping relationship between two ophthalmic imaging modalities.
The lens opacity is an important indicator to measure the severity level of
cataracts, which are presented on different ophthalmic images through
different forms in clinical research. Therefore, it is potential to construct
the mapping relationship between two different ophthalmic imaging modalities
to develop a new cataract classification/grading protocol by comparing the
lens opacities, e.g., fundus image-based cataract classification system WHO
Cataract Grading System.

Furthermore, to verify the effectiveness of new standard cataract grading
protocols, we must collect multiple center data from hospitals in different
regions.

###  7.3 How to annotate cataract images accurately

Data annotation is a challenging problem for the medical image analysis field
including cataract image analysis, since it is the significant base for
accurate ML-based disease diagnosis. However, clinicians cannot label massive
cataract images manually Resnikoff et al. (2020, 2012), because it is
expensive, time-consuming, and objective. To address this challenges, we offer
the following solutions:

  * •

Semi-supervised learning: Song et al. (2016, 2019) uses the semi-supervised
learning strategy to recognize cataract severity levels on fundus images and
achieves expected performance. It is probably to utilize weakly supervised
learning methods to learn useful information from labeled cataract images and
let the method automatically label unlabeled cataract images according to
learned information.

  * •

Unsupervised learning: Recent works have shown that deep
clustering/unsupervised learning techniques can help researchers acquire
labels positively rather than acquire labels negatively Ravi and Larochelle
(2016); Grira et al. (2004); Fogel et al. (2019); Guo et al. (2017b); Hsu and
Kira (2015). Thus, We can actively apply deep clustering/unsupervised learning
methods to label cataract images in the future.

  * •

Content-based image retrieval: the content-based image retrieval (CBIR)
Ramamurthy and Chandran (2011); Sivakamasundari et al. (2014); Fathabad and
Balafar (2012); Fang et al. (2020) technique has been widely used for
different tasks based on different image features, which also can be utilized
to annotate cataract images by comparing testing images with standard images.

###  7.4 How to classify/grade cataract accurately for precise cataract
diagnosis

Most previous works focused on cataract screening, and few works considered
clinical cataract diagnosis, especially for cataract surgery planning. This is
because different cataract severity levels and cataract types should
clinically take the corresponding treatments. Hence, it is necessary to
develop state-of-the-art methods to classify cataract severity levels
accurately, and this survey provides the following research directions.

  * •

Clinical prior knowledge injection: Furthermore, cataracts are associated with
various factors Lin et al. (2020), e.g., sub-lens regions Xu et al. (2016);
Gao et al. (2011); Caxinha et al. (2015); Grulkowski et al. (2018); Pawliczek
et al. (2020), which can be considered as domain knowledge of cataract. Thus,
we can infuse the domain knowledge into deep networks for automatic cataract
classification/grading according to the characteristics of ophthalmic images.
E.g., Zhang et al. (2022b) incorporates clinical features in attention-based
network design for classification.

  * •

Multi-task learning for classification and segmentation: Over the past
decades, multi-task learning techniques have been successfully applied to
various fields, including medical image analysis. Xu et al. Xu et al. (2019a)
used the Faster-RCNN framework to detect the lens region and grade nuclear
cataract severity levels on slit lamp images and achieved competitive
performance. Literature Yin et al. (2018); Zhang et al. (2019b) proposes the
deep segmentation network framework for automatic lens subregion segmentation
based on AS-OCT images, which is a significant base for cataract diagnosis and
cataract surgery planning. Moreover, more multi-task learning frameworks
should be developed for cataract classification and lens segmentation,
considering multi-task learning framework usually keeps a good balance between
performance and complexity.

  * •

Transfer learning: Over the years, researchers have used the transfer learning
method to improve the cataract classification performance Xu et al. (2019b, a)
with pre-trained CNNs. Large deep neural network models usually perform better
than small deep neural network models based on massive data, which previous
works have demonstrated. However, it is challenging to collect massive data in
the medical field; thus, it is vital for us to develop transfer learning
strategies to take full use of pre-trained parameters for large deep neural
network models, to further improve the performance of cataract-related tasks.

  * •

Multimodality learning: Previous works only used ophthalmic image type for
cataract diagnosis Xu et al. (2016); Gao et al. (2011); Caxinha et al. (2015);
Yang et al. (2013), multimodality learning Cardoso et al. (2017); Ma et al.
(2019); Guo et al. (2019); Cheng et al. (2015) have been utilized to tackle
different medical image tasks, which also can be used for automatic cataract
classification/grading based on multi-ophthalmic images or the combination of
ophthalmic images and non-image data. Multimodality data can be classified
into image data and non-image data. However, it is challenging to use
multimodality images for automatic cataract classification. Two reasons can
account for it: 1) only silt lamp images and fundus images have standard
cataract classification systems, which have no correlation relationship
between them; 2) existing classification systems are subjective, it is
challenging for clinicians to label two cataract severity levels correctly for
different ophthalmic images. Furthermore, we can combine image data and non-
image data for automatic cataract diagnosis because it is easy to collect non-
image data such as age and sex associated with cataracts. Furthermore, recent
studies have Mohammadi et al. (2012); Lin et al. (2020) used non-image data
for PC and PCO diagnosis, which demonstrated that it is potential to use
multimodality data to improve the precision of cataract diagnosis

  * •

Image denoising: Image noise is an important factor in affecting automatic
cataract diagnosis on ophthalmic images. Researchers have proposed different
methods to remove the noise from the images based on the characteristics of
ophthalmic images, such as Gaussian filter, discrete wavelet transform (DWT),
discrete cosine transform (DCT), Haar wavelet transform, and its variants Zhou
et al. (2019); Guo et al. (2015); Fuadah et al. (2015). Additionally, recent
research has begun to use the GAN model for medical image denoising and
achieved good results.

###  7.5 Improving the interpretability of deep learning methods

Deep learning methods have been widely used for cataract
classification/grading. However, deep learning methods are still considered a
black box without explaining why they make good prediction results or poor
prediction results. Therefore, it is necessary to give reliable explanations
for the predicted results based on deep learning methods. Literature Zhou et
al. (2019) visualizes weight distributions of deep convolutional layers and
dense layers to explain the predicted results of deep learning methods. It is
likely to analyze the similarities between the weights of convolutional layers
to describe the predicted results.

Zhang et al. (2019b) indicated that infusing the domain knowledge into
networks can improve the cataract segmentation results based on AS-OCT images
and enhance the interpretability. It is promising for researchers to combine
deep learning methods with different domain knowledge to enhance the
interpretability of deep networks.

Modern neural networks Guo et al. (2017a) usually are poorly calibrated,
Müller et al. (2019) indicated that improving the confidence of predicted
probability can explain the predicted results. It is a potential solution for
researchers to enhance interpretability by enhancing the confidence of
predicted probability.

Moreover, researchers have achieved great progress in the interpretability of
deep learning methods in the computer vision field.Zhou et al. (2016);
Selvaraju et al. (2017); Guidotti et al. (2018); Lu et al. (2017) used the
heat maps to localize discriminative regions where CNNs learned. Hinton et al.
Kornblith et al. (2019) proposed the correlation analysis technique to analyze
similarities of neural network representations to explain what neural networks
had learned. These interpretability techniques are also used for deep
learning-based cataract classification works for improving the
interpretability.

###  7.6 Moblie cataract screening

In the past years, researchers have used digital camera images for cataract
screening based on ML techniques and have achieved good results Supriyanti and
Ramadhani (2011); Patwari et al. (2011); Fuadah et al. (2015); Supriyanti and
Ramadhani (2012); Pathak and Kumar (2016); Rana and Galib (2017); Fuadah et
al. (2015). Digital camera images can be accessed through mobile phones, which
are widely used worldwide. Thus, there are endless potentials to embed
powerful digital cameras into mobile phones for cataract screening. Although
deep learning methods can achieve excellent cataract screening results on
digital camera images, their parameters are too much. One possible solution is
to develop lightweight network models. Researchers have achieved significant
improvement on designing lightweight deep learning methods over the years
Cheng et al. (2017); Jiang et al. (2019); He et al. (2017), which demonstrated
that deep networks kept a good balance between accuracy and speed, which are
important for developing cataract screening-based lightweight deep networks.

###  7.7 How to evaluate the generalization ability of machine learning
methods for other eye disease classification tasks

Although researchers have proposed massive machine learning techniques for
automatic cataract classification/grading, including conventional machine
learning methods and deep learning methods, and obtained promising performance
on different eye images. However, only Zhang et al. Zhang et al. (2022b)
validated the generalization ability of the proposed attention-based network
on cataract and age-related macular degeneration (AMD) classification task,
while others focused on the pure cataract classification task. The following
reasons can explain that: 1) due to characteristics of eye images, it is
difficult to validate the effectiveness of a method on different eye disease
classification tasks, especially for conventional machine learning methods. 2)
most existing works focused on specific eye disease tasks like cataract and
glaucoma rather than universal methods for medical analysis. 3) Appropriate
open-access medical datasets are rare. 4) None previous works have
systematically concluded the existing methods and built standard baselines.

This survey offers the following solutions:

  * •

More open-access medical datasets are supposed to be released.

  * •

it is significant to build baselines of different eye disease tasks, which can
provide the standard benchmarks for others to follow.

###  7.8 Others

Apart from the above-mentioned challenges, there are other challenges for
medical image analysis, including cataract, such as label noises Song et al.
(2022), domain distribution shift across different datasets, and how to train
more robust models. However, these challenges have not been studied in the
cataract classification/grading field. Considering the real diagnosis
requirements, it is necessary to address these challenges by following
advanced machine learning methods, such as graph convolution method Zhong et
al. (2019), knowledge distillation, adversarial learning, etc.

##  8 Conclusion

Automatic and objective cataract classification/grading is an important and
hot research topic, helping reduce blindness ratio and improve life quality.
In this survey, we survey the recent advances in the ML-based cataract
classification/grading on ophthalmic imaging modalities, and highlight two
research fields: conventional ML and deep learning methods. Though ML
techniques have made significant progress in cataract classification and
grading, there is still room for improvement. First, public cataract
ophthalmic images are needed, which can help build ML baselines. Secondly,
automatic ophthalmic image annotation has not been studied seriously and is a
potential research direction. Thirdly, developing accurate and objective ML
methods for clinical cataract diagnosis is efficient to help more people
improve their vision. It is necessary to enhance the interpretability of deep
learning, which can prompt applications in cataract diagnosis. Finally,
previous works have used digital camera images for cataract screening, which
is a good idea by devising lightweight ML techniques that address the cataract
screening problem for developing countries with limited access to expensive
ophthalmic devices.

## Abbreviation

CAD: computer aided diagnosis; ML: machine learning; WHO: World Health
Organization; NC: nuclear cataract; CC: cortical cataract; PSC: posterior
subcapsular cataract; LR: linear regression ; RF: random forest; SVR: support
vector machine regression; ASM: active shape model; NLP: natural language
processing; OCT: optical coherence tomography; LOCS: Lens Opacity
Classification System; OCCCGS: Oxford Clinical Cataract Classification and
Grading System; SVM: support vector machine; artificial intelligent: AI; BOF:
bag-of-features; PCA: principal component analysis; DWT: discrete wavelet
transform; DCT: discrete cosine transform; KNN: K-nearest neighbor; ANN:
artificial neural network; BPNN: back propagation neural network; CNN:
convolutional neural network; RNN: recurrent neural network; DNN: deep neural
network; MLP: multilayer perceptron; ROI: region of interes; AE: autoencoder;
GAN: generative adversarial network; CBIR: content-based image retrieval;
ASOCT: anterior segment ptical coherence tomography; 3D: three-dimensional;
2D: two-dimensional; SSL:Semi-supervised learning; AWM: Adaptive window model;
GSR: Group Sparsity Regression; GLCM: gray level Co-occurrence matrix; Image
morphological feature: IMF; SWLR: Similarity Weighted Linear Reconstruction;
CBAM: Convolutional block attention module; ECA: Efficient channel attention;
SE: squeeze-and-excitation; SPA: spatial pyramid attention.

## References

  * Abràmoff et al. (2016) Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative ophthalmology & visual science 57(13):5200–5206
  * Ang et al. (2018) Ang M, Baskaran M, Werkmeister RM, Chua J, Schmidl D, dos Santos] VA, Garhöfer G, Mehta JS, Schmetterer L (2018) Anterior segment optical coherence tomography. Progress in Retinal and Eye Research 66:132 – 156, DOI https://doi.org/10.1016/j.preteyeres.2018.04.002, URL http://www.sciencedirect.com/science/article/pii/S135094621730085X
  * Asbell et al. (2005) Asbell PA, Dualan I, Mindel J, Brocks D, Ahmad M, Epstein S (2005) Age-related cataract. The Lancet 365(9459):599–609
  * for the Prevention of Blindness and Group (2001) for the Prevention of Blindness WP, Group WCG (2001) A simplified cataract grading system
  * Bourne et al. (2017) Bourne RR, Flaxman SR, Braithwaite T, Cicinelli MV, Das A, Jonas JB, Keeffe J, Kempen JH, Leasher J, Limburg H, et al. (2017) Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis. The Lancet Global Health 5(9):e888–e897
  * Caixinha et al. (2014a) Caixinha M, Jesus DA, Velte E, Santos MJ, Santos JB (2014a) Using ultrasound backscattering signals and nakagami statistical distribution to assess regional cataract hardness. IEEE Transactions on Biomedical Engineering 61(12):2921–2929
  * Caixinha et al. (2014b) Caixinha M, Velte E, Santos M, Santos JB (2014b) New approach for objective cataract classification based on ultrasound techniques using multiclass svm classifiers. In: 2014 IEEE International Ultrasonics Symposium, IEEE, pp 2402–2405
  * Caixinha et al. (2016) Caixinha M, Amaro J, Santos M, Perdigão F, Gomes M, Santos J (2016) In-vivo automatic nuclear cataract detection and classification in an animal model by ultrasounds. IEEE Transactions on Biomedical Engineering 63(11):2326–2335
  * Cao et al. (2020a) Cao G, Zhao W, Higashita R, Liu J, Chen W, Yuan J, Zhang Y, Yang M (2020a) An efficient lens structures segmentation method on as-oct images. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, pp 1646–1649
  * Cao et al. (2020b) Cao L, Li H, Zhang Y, Zhang L, Xu L (2020b) Hierarchical method for cataract grading based on retinal images using improved haar wavelet. Information Fusion 53:196–208
  * Cardoso et al. (2017) Cardoso MJ, Arbel T, Carneiro G, Syeda-Mahmood T, Tavares JMR, Moradi M, Bradley A, Greenspan H, Papa JP, Madabhushi A, et al. (2017) Deep learning in medical image analysis and multimodal learning for clinical decision support
  * Carmona et al. (2008) Carmona EJ, Rincón M, García-Feijoó J, Martínez-de-la Casa JM (2008) Identification of the optic nerve head with genetic algorithms. Artificial Intelligence in Medicine 43(3):243–259
  * Caxinha et al. (2015) Caxinha M, Velte E, Santos M, Perdigão F, Amaro J, Gomes M, Santos J (2015) Automatic cataract classification based on ultrasound technique using machine learning: a comparative study. Physics Procedia 70:1221–1224
  * Chen et al. (2015) Chen H, Dou Q, Ni D, Cheng JZ, Qin J, Li S, Heng PA (2015) Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 507–514
  * Cheng et al. (2015) Cheng B, Liu M, Suk HI, Shen D, Zhang D, Initiative ADN, et al. (2015) Multimodal manifold-regularized transfer learning for mci conversion prediction. Brain imaging and behavior 9(4):913–926
  * Cheng (2018) Cheng J (2018) Sparse range-constrained learning and its application for medical image grading. IEEE Transactions on Medical Imaging 37(12):2729–2738
  * Cheng et al. (2017) Cheng Y, Wang D, Zhou P, Zhang T (2017) A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:171009282
  * Cheung et al. (2011) Cheung CYl, Li H, Lamoureux EL, Mitchell P, Wang JJ, Tan AG, Johari LK, Liu J, Lim JH, Aung T, et al. (2011) Validity of a new computer-aided diagnosis imaging program to quantify nuclear cataract from slit-lamp photographs. Investigative ophthalmology & visual science 52(3):1314–1319
  * Chew et al. (2012) Chew M, Chiang PPC, Zheng Y, Lavanya R, Wu R, Saw SM, Wong TY, Lamoureux EL (2012) The impact of cataract, cataract types, and cataract grades on vision-specific functioning using rasch analysis. American journal of Ophthalmology 154(1):29–38
  * Choi et al. (2020) Choi M, Kim H, Han B, Xu N, Lee KM (2020) Channel attention is all you need for video frame interpolation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 10663–10671
  * Chow et al. (2011) Chow YC, Gao X, Li H, Lim JH, Sun Y, Wong TY (2011) Automatic detection of cortical and psc cataracts using texture and intensity analysis on retro-illumination lens images. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp 5044–5047
  * Chylack et al. (1988) Chylack LT, Leske MC, Sperduto R, Khu P, McCarthy D (1988) Lens opacities classification system. Archives of Ophthalmology 106(3):330–334
  * Chylack et al. (1989) Chylack LT, Leske MC, McCarthy D, Khu P, Kashiwagi T, Sperduto R (1989) Lens opacities classification system ii (locs ii). Archives of ophthalmology 107(7):991–997
  * Chylack et al. (1993) Chylack LT, Wolfe JK, Singer DM, Leske MC, Bullimore MA, Bailey IL, Friend J, McCarthy D, Wu SY (1993) The lens opacities classification system iii. Archives of ophthalmology 111(6):831–836
  * Cuadros and Bresnick (2009) Cuadros J, Bresnick G (2009) Eyepacs: an adaptable telemedicine system for diabetic retinopathy screening. Journal of diabetes science and technology 3(3):509–516
  * Decenciere et al. (2013) Decenciere E, Cazuguel G, Zhang X, Thibault G, Klein JC, Meyer F, Marcotegui B, Quellec G, Lamard M, Danno R, et al. (2013) Teleophta: Machine learning and image processing methods for teleophthalmology. Irbm 34(2):196–203
  * Decencière et al. (2014) Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A, et al. (2014) Feedback on a publicly distributed image database: the messidor database. Image Analysis & Stereology 33(3):231–234
  * Deng et al. (2018) Deng L, Jiao P, Pei J, Wu Z, Li G (2018) Gxnor-net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework. Neural Networks 100:49–58
  * Dong et al. (2017) Dong Y, Zhang Q, Qiao Z, Yang JJ (2017) Classification of cataract fundus image based on deep learning. In: 2017 IEEE International Conference on Imaging Systems and Techniques (IST), IEEE, pp 1–5
  * Dosovitskiy et al. (2020) Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al. (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929
  * Fan et al. (2003) Fan S, Dyer CR, Hubbard L, Klein B (2003) An automatic system for classification of nuclear sclerosis from slit-lamp photographs. In: Ellis RE, Peters TM (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003, Springer Berlin Heidelberg, Berlin, Heidelberg, pp 592–601
  * Fan et al. (2015) Fan W, Shen R, Zhang Q, Yang JJ, Li J (2015) Principal component analysis based cataract grading and classification. In: 2015 17th International Conference on E-health Networking, Application & Services (HealthCom), IEEE, pp 459–462
  * Fang et al. (2020) Fang J, Xu Y, Zhang X, Hu Y, Liu J (2020) Attention-based saliency hashing for ophthalmic image retrieval. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 990–995, DOI 10.1109/BIBM49941.2020.9313536
  * Fathabad and Balafar (2012) Fathabad YF, Balafar M (2012) Content based image retrieval for medical images. International Journal on Technical and Physical Problems of Engineering (IJTPE) (12):177–182
  * Fercher et al. (1993) Fercher AF, Li HC, Hitzenberger CK (1993) Slit lamp laser doppler interferometer. Lasers in Surgery and Medicine 13(4):447–452
  * Fogel et al. (2019) Fogel S, Averbuch-Elor H, Cohen-Or D, Goldberger J (2019) Clustering-driven deep embedding with pairwise constraints. IEEE computer graphics and applications 39(4):16–27
  * Foong et al. (2007) Foong AWP, Saw SM, Loo JL, Shen S, Loon SC, Rosman M, Aung T, Tan DTH, Tai ES, Wong TY (2007) Rationale and methodology for a population-based study of eye diseases in malay people: The singapore malay eye study (simes). Ophthalmic Epidemiology 14(1):25–35, DOI 10.1080/09286580600878844, URL https://doi.org/10.1080/09286580600878844, https://doi.org/10.1080/09286580600878844
  * Fu et al. (2016) Fu H, Xu Y, Lin S, Wong DWK, Liu J (2016) Deepvessel: Retinal vessel segmentation via deep learning and conditional random field. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 132–139
  * Fuadah et al. (2015) Fuadah YN, Setiawan AW, Mengko T (2015) Performing high accuracy of the system for cataract detection using statistical texture analysis and k-nearest neighbor. In: 2015 International Seminar on Intelligent Technology and Its Applications (ISITIA), IEEE, pp 85–88
  * Fuadah et al. (2015) Fuadah YN, Setiawan AW, Mengko TLR, Budiman (2015) Mobile cataract detection using optimal combination of statistical texture analysis. In: 2015 4th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), pp 232–236, DOI 10.1109/ICICI-BME.2015.7401368
  * Gali et al. (2019) Gali HE, Sella R, Afshari NA (2019) Cataract grading systems: a review of past and present. Current opinion in ophthalmology 30(1):13–18
  * Gao et al. (2011) Gao X, Li H, Lim JH, Wong TY (2011) Computer-aided cataract detection using enhanced texture features on retro-illumination lens images. In: 2011 18th IEEE International Conference on Image Processing, pp 1565–1568
  * Gao et al. (2015) Gao X, Lin S, Wong TY (2015) Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Transactions on Biomedical Engineering 62(11):2693–2701
  * Gershenzon and Robman (1999) Gershenzon A, Robman L (1999) New software for lens retro-illumination digital image analysis. Australian and New Zealand Journal of Ophthalmology 27(3-4):170–172
  * Goh et al. (2020) Goh JHL, Lim ZW, Fang X, Anees A, Nusinovici S, Rim TH, Cheng CY, Tham YC (2020) Artificial intelligence for cataract detection and management. The Asia-Pacific Journal of Ophthalmology 9(2):88–95
  * Grewal et al. (2018) Grewal PS, Oloumi F, Rubin U, Tennant MT (2018) Deep learning in ophthalmology: a review. Canadian Journal of Ophthalmology 53(4):309–313
  * Grira et al. (2004) Grira N, Crucianu M, Boujemaa N (2004) Unsupervised and semi-supervised clustering: a brief survey. A review of machine learning techniques for processing multimedia content 1:9–16
  * Group et al. (2001) Group AREDSR, et al. (2001) The age-related eye disease study (areds) system for classifying cataracts from photographs: Areds report no. 4. American journal of ophthalmology 131(2):167–175
  * Grulkowski et al. (2018) Grulkowski I, Manzanera S, Cwiklinski L, Mompeán J, de Castro A, Marin JM, Artal P (2018) Volumetric macro- and micro-scale assessment of crystalline lens opacities in cataract patients using long-depth-range swept source optical coherence tomography. Biomed Opt Express 9(8):3821–3833, DOI 10.1364/BOE.9.003821, URL http://www.osapublishing.org/boe/abstract.cfm?URI=boe-9-8-3821
  * Guidotti et al. (2018) Guidotti R, Monreale A, Ruggieri S, Turini F, Giannotti F, Pedreschi D (2018) A survey of methods for explaining black box models. ACM computing surveys (CSUR) 51(5):1–42
  * Gulati et al. (2020) Gulati A, Qin J, Chiu CC, Parmar N, Zhang Y, Yu J, Han W, Wang S, Zhang Z, Wu Y, et al. (2020) Conformer: Convolution-augmented transformer for speech recognition. arXiv preprint arXiv:200508100
  * Guo et al. (2017a) Guo C, Pleiss G, Sun Y, Weinberger KQ (2017a) On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, JMLR. org, pp 1321–1330
  * Guo et al. (2015) Guo L, Yang JJ, Peng L, Li J, Liang Q (2015) A computer-aided healthcare system for cataract classification and grading based on fundus image analysis. Computers in Industry 69:72–80
  * Guo et al. (2021) Guo MH, Xu TX, Liu JJ, Liu ZN, Jiang PT, Mu TJ, Zhang SH, Martin RR, Cheng MM, Hu SM (2021) Attention mechanisms in computer vision: A survey. arXiv preprint arXiv:211107624
  * Guo et al. (2017b) Guo X, Liu X, Zhu E, Yin J (2017b) Deep clustering with convolutional autoencoders. In: International Conference on Neural Information Processing, Springer, pp 373–382
  * Guo et al. (2019) Guo Z, Li X, Huang H, Guo N, Li Q (2019) Deep learning-based image segmentation on multimodal medical imaging. IEEE Transactions on Radiation and Plasma Medical Sciences 3(2):162–169, DOI 10.1109/TRPMS.2018.2890359
  * Hall et al. (1997) Hall A, Thompson J, Deane J, Rosenthal A (1997) Locs iii versus the oxford clinical cataract classification and grading system for the assessment of nuclear, cortical and posterior subcapsular cataract. Ophthalmic epidemiology 4(4):179–194
  * He et al. (2022) He K, Gan C, Li Z, Rekik I, Yin Z, Ji W, Gao Y, Wang Q, Zhang J, Shen D (2022) Transformers in medical image analysis: A review. arXiv preprint arXiv:220212165
  * He et al. (2017) He Y, Zhang X, Sun J (2017) Channel pruning for accelerating very deep neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1389–1397
  * Hernandez-Matas et al. (2017) Hernandez-Matas C, Zabulis X, Triantafyllou A, Anyfanti P, Douma S, Argyros AA (2017) Fire: fundus image registration dataset. Modeling and Artificial Intelligence in Ophthalmology 1(4):16–28
  * Hirnschall et al. (2013) Hirnschall N, Amir-Asgari S, Maedel S, Findl O (2013) Predicting the postoperative intraocular lens position using continuous intraoperative optical coherence tomography measurements. Investigative ophthalmology & visual science 54(8):5196–5203
  * Hirnschall et al. (2017) Hirnschall N, Buehren T, Bajramovic F, Trost M, Teuber T, Findl O (2017) Prediction of postoperative intraocular lens tilt using swept-source optical coherence tomography. Journal of Cataract & Refractive Surgery 43(6):732–736
  * Hoover et al. (2000) Hoover A, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical imaging 19(3):203–210
  * Hsu and Kira (2015) Hsu YC, Kira Z (2015) Neural network-based clustering using pairwise constraints. arXiv preprint arXiv:151106321
  * Hu et al. (2020) Hu S, Wang X, Wu H, Luan X, Qi P, Lin Y, He X, He W (2020) Unified diagnosis framework for automated nuclear cataract grading based on smartphone slit-lamp images. IEEE Access 8:174169–174178, DOI 10.1109/ACCESS.2020.3025346
  * Hu et al. (2021) Hu S, Luan X, Wu H, Wang X, Yan C, Wang J, Liu G, He W (2021) Accv: automatic classification algorithm of cataract video based on deep learning. BioMedical Engineering OnLine 20(1):1–17
  * Hu et al. (2020) Hu Y, Zhang XQ, Xu L, He FX, Tian Z, She W, Liu W (2020) Harmonic loss function for sensor-based human activity recognition based on lstm recurrent neural networks. IEEE Access 8:135617–135627
  * Huang et al. (2007) Huang CC, Ameri H, DeBoer C, Rowley AP, Xu X, Sun L, Wang SH, Humayun MS, Shung KK (2007) Evaluation of lens hardness in cataract surgery using high-frequency ultrasonic parameters in vitro. Ultrasound in medicine & biology 33(10):1609–1616
  * Huang et al. (2009a) Huang CC, Chen R, Tsui PH, Zhou Q, Humayun MS, Shung KK (2009a) Measurements of attenuation coefficient for evaluating the hardness of a cataract lens by a high-frequency ultrasonic needle transducer. Physics in Medicine & Biology 54(19):5981
  * Huang et al. (2017) Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708
  * Huang et al. (2009b) Huang W, Li H, Chan KL, Lim JH, Liu J, Wong TY (2009b) A computer-aided diagnosis system of nuclear cataract via ranking. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 803–810
  * Huang et al. (2010) Huang W, Chan KL, Li H, Lim JH, Liu J, Wong TY (2010) A computer assisted method for nuclear cataract grading from slit-lamp images using ranking. IEEE Transactions on Medical Imaging 30(1):94–107
  * Huiqi Li et al. (2008) Huiqi Li, Joo Hwee Lim, Jiang Liu, Wing Kee Wong D, Wong TY (2008) Computer aided diagnosis of nuclear cataract. In: 2008 3rd IEEE Conference on Industrial Electronics and Applications, pp 1841–1844, DOI 10.1109/ICIEA.2008.4582838
  * Huo et al. (2019) Huo C, Akhtar F, Li P (2019) A novel grading method of cataract based on awm. In: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), IEEE, vol 2, pp 368–373
  * Imran et al. (2021) Imran A, Li J, Pei Y, Akhtar F, Mahmood T, Zhang L (2021) Fundus image-based cataract classification using a hybrid convolutional and recurrent neural network. The Visual Computer 37(8):2407–2417
  * Jagadale et al. (2019) Jagadale A, Sonavane S, Jadav D (2019) Computer aided system for early detection of nuclear cataract using circle hough transform. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), IEEE, pp 1009–1012
  * Jesus et al. (2013) Jesus D, Velte E, Caixinha M, Santos M, Santos J (2013) Using of the ultrasound frequency dependent attenuation and nakagami distribution for cataract evaluation. In: 2013 IEEE 3rd Portuguese Meeting in Bioengineering (ENBENG), pp 1–4
  * Jiang et al. (2017) Jiang J, Liu X, Zhang K, Long E, Wang L, Li W, Liu L, Wang S, Zhu M, Cui J (2017) Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network. Biomedical Engineering Online 16(1):132
  * Jiang et al. (2018) Jiang J, Liu X, Liu L, Wang S, Long E, Yang H, Yuan F, Yu D, Zhang K, Wang L, et al. (2018) Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network. PloS one 13(7):e0201142
  * Jiang et al. (2021a) Jiang J, Lei S, Zhu M, Li R, Yue J, Chen J, Li Z, Gong J, Lin D, Wu X, et al. (2021a) Improving the generalizability of infantile cataracts detection via deep learning-based lens partition strategy and multicenter datasets. Frontiers in Medicine 8:470
  * Jiang et al. (2021b) Jiang J, Wang L, Fu H, Long E, Sun Y, Li R, Li Z, Zhu M, Liu Z, Chen J, et al. (2021b) Automatic classification of heterogeneous slit-illumination images using an ensemble of cost-sensitive convolutional neural networks. Annals of Translational Medicine 9(7)
  * Jiang et al. (2019) Jiang Y, Wang S, Valls V, Ko BJ, Lee WH, Leung KK, Tassiulas L (2019) Model pruning enables efficient federated learning on edge devices. arXiv preprint arXiv:190912326
  * Jun et al. (2019) Jun TJ, Eom Y, Kim C, Kim D, et al. (2019) Tournament based ranking cnn for the cataract grading. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp 1630–1636
  * Junayed et al. (2021) Junayed MS, Islam MB, Sadeghzadeh A, Rahman S (2021) Cataractnet: An automated cataract detection system using deep learning for fundus images. IEEE Access 9:128799–128808
  * Kai et al. (2019) Kai, Zhang, Xiyang, Liu, Jiewei, Jiang, Wangting, Shuai, Wang, Lin (2019) Prediction of postoperative complications of pediatric cataract patients using data mining. Journal of Translational Medicine
  * Kauppi et al. (2006) Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Uusitalo H, Kälviäinen H, Pietilä J (2006) Diaretdb0: Evaluation database and methodology for diabetic retinopathy algorithms. Machine Vision and Pattern Recognition Research Group, Lappeenranta University of Technology, Finland 73:1–17
  * Khan et al. (2018) Khan AA, Akram MU, Tariq A, Tahir F, Wazir K (2018) Automated computer aided detection of cataract. In: Abraham A, Haqiq A, Ella Hassanien A, Snasel V, Alimi AM (eds) Proceedings of the Third International Afro-European Conference for Industrial Advancement — AECIA 2016, Springer International Publishing, Cham, pp 340–349
  * Klein et al. (1990) Klein BEK, Klein R, Linton KLP, Magli YL, Neider MW (1990) Assessment of cataracts from photographs in the beaver dam eye study. Ophthalmology 97(11):1428–1433
  * Kornblith et al. (2019) Kornblith S, Norouzi M, Lee H, Hinton G (2019) Similarity of neural network representations revisited
  * Krizhevsky et al. (2012) Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
  * Li et al. (2007) Li H, Lim JH, Liu J, Wong TY (2007) Towards automatic grading of nuclear cataract. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp 4961–4964
  * Li et al. (2008a) Li H, Ko L, Lim JH, Liu J, Wong DWK, Wong TY (2008a) Image based diagnosis of cortical cataract. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp 3904–3907
  * Li et al. (2008b) Li H, Ko L, Lim JH, Liu J, Wong DWK, Wong TY, Sun Y (2008b) Automatic opacity detection in retro-illumination images for cortical cataract diagnosis. In: 2008 IEEE International Conference on Multimedia and Expo, IEEE, pp 553–556
  * Li et al. (2009) Li H, Lim JH, Liu J, Wong DWK, Tan NM, Lu S, Zhang Z, Wong TY (2009) An automatic diagnosis system of nuclear cataract using slit-lamp images. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp 3693–3696
  * Li et al. (2010a) Li H, Lim JH, Liu J, Mitchell P, Tan AG, Wang JJ, Wong TY (2010a) A computer-aided diagnosis system of nuclear cataract. IEEE Transactions on Biomedical Engineering 57(7):1690–1698
  * Li et al. (2010b) Li H, Lim JH, Liu J, Wong DWK, Foo Y, Sun Y, Wong TY (2010b) Automatic detection of posterior subcapsular cataract opacity for cataract screening. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, IEEE, pp 5359–5362
  * Lian et al. (2021) Lian D, Yu Z, Sun X, Gao S (2021) As-mlp: An axial shifted mlp architecture for vision. arXiv preprint arXiv:210708391
  * Lin et al. (2020) Lin D, Chen J, Lin Z, Li X, Zhang K, Wu X, Liu Z, Huang J, Li J, Zhu Y, et al. (2020) A practical model for the identification of congenital cataracts using machine learning. EBioMedicine 51:102621
  * Linglin Zhang et al. (2017) Linglin Zhang, Jianqiang Li, i Zhang, He Han, Bo Liu, Yang J, Qing Wang (2017) Automatic cataract detection and grading using deep convolutional neural network. In: 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), pp 60–65
  * Litjens et al. (2017) Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Medical image analysis 42:60–88
  * Liu et al. (2013) Liu J, Wong DWK, Zhang Z, Lee BH, Gao X, Yin F, Zhang J, Htoo MT (2013) Integrating research, clinical practice and translation: The singapore experience. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp 7148–7151
  * Liu et al. (2017a) Liu X, Jiang J, Zhang K, Long E, Cui J, Zhu M, An Y, Zhang J, Liu Z, Lin Z, et al. (2017a) Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network. PloS one 12(3):e0168606
  * Liu et al. (2017b) Liu YC, Wilkins M, Kim T, Malyugin B, Mehta JS (2017b) Cataracts. The Lancet 390(10094):600–612
  * Long et al. (2017) Long E, Lin H, Liu Z, Wu X, Wang L, Jiang J, An Y, Lin Z, Li X, Chen J, et al. (2017) An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nature biomedical engineering 1(2):1–8
  * Lopez et al. (2016) Lopez HIM, Garcia JCS, Mendez JAD (2016) Cataract detection techniques: A review. IEEE Latin America Transactions 14(7):3074–3079
  * Lu et al. (2017) Lu J, Xiong C, Parikh D, Socher R (2017) Knowing when to look: Adaptive attention via a visual sentinel for image captioning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 375–383
  * Ma et al. (2019) Ma J, Zhao Z, Chen J, Li A, Hong L, Chi E (2019) Snr: Sub-network routing for flexible parameter sharing in multi-task learning. Proceedings of the AAAI Conference on Artificial Intelligence 33:216–223, DOI 10.1609/aaai.v33i01.3301216
  * Mahmudi et al. (2014) Mahmudi T, Kafieh R, Rabbani H, Akhlagi M, et al. (2014) Comparison of macular octs in right and left eyes of normal people. In: Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, SPIE, vol 9038, pp 472–477
  * Mansoor et al. (2016) Mansoor A, Cerrolaza JJ, Idrees R, Biggs E, Alsharid MA, Avery RA, Linguraru MG (2016) Deep learning guided partitioned shape model for anterior visual pathway segmentation. IEEE transactions on medical imaging 35(8):1856–1865
  * Mansour et al. (2021) Mansour Y, Lin K, Heckel R (2021) Image-to-image mlp-mixer for image reconstruction
  * Mohammadi et al. (2012) Mohammadi SF, Sabbaghi M, Hadi Z, Hashemi H, Alizadeh S, Majdi M, Taee F, et al. (2012) Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification. Journal of Cataract & Refractive Surgery 38(3):403–408
  * Müller et al. (2019) Müller R, Kornblith S, Hinton G (2019) When does label smoothing help? arXiv preprint arXiv:190602629
  * Pascolini and Mariotti (2012) Pascolini D, Mariotti SP (2012) Global estimates of visual impairment: 2010. British Journal of Ophthalmology 96(5):614–618
  * Pathak and Kumar (2016) Pathak S, Kumar B (2016) A robust automated cataract detection algorithm using diagnostic opinion based parameter thresholding for telemedicine application. Electronics 5(3):57
  * Patwari et al. (2011) Patwari MAU, Arif MD, Chowdhury MN, Arefin A, Imam MI (2011) Detection, categorization, and assessment of eye cataracts using digital image processing. In: The first international conference on interdisciplinary research and development, pp 22–1
  * Pawliczek et al. (2020) Pawliczek D, Dalke C, Fuchs H, Gailus-Durner V, de Angelis MH, Graw J, Amarie OV (2020) Spectral domain-optical coherence tomography (sd-oct) as a monitoring tool for alterations in mouse lenses. Experimental eye research 190:107871
  * Plesch et al. (1987) Plesch A, Klingbeil U, Bille J (1987) Digital laser scanning fundus camera. Applied optics 26(8):1480–1486
  * Pomerantzeff et al. (1979) Pomerantzeff O, Webb RH, Delori FC (1979) Image formation in fundus cameras. Investigative ophthalmology & visual science 18(6):630–637
  * Porwal et al. (2018) Porwal P, Pachade S, Kamble R, Kokare M, Deshmukh G, Sahasrabuddhe V, Meriaudeau F (2018) Indian diabetic retinopathy image dataset (idrid): a database for diabetic retinopathy screening research. Data 3(3):25
  * Pratap and Kokil (2019) Pratap T, Kokil P (2019) Computer-aided diagnosis of cataract using deep transfer learning. Biomedical Signal Processing and Control 53:101533
  * Pratap and Kokil (2021) Pratap T, Kokil P (2021) Efficient network selection for computer-aided cataract diagnosis under noisy environment. Computer Methods and Programs in Biomedicine 200:105927
  * Qiao et al. (2017) Qiao Z, Zhang Q, Dong Y, Yang JJ (2017) Application of svm based on genetic algorithm in classification of cataract fundus images. In: 2017 IEEE International Conference on Imaging Systems and Techniques (IST), IEEE, pp 1–5
  * Ramamurthy and Chandran (2011) Ramamurthy B, Chandran K (2011) Content based image retrieval for medical images using canny edge detection algorithm. International Journal of Computer Applications 17(6):32–37
  * Rana and Galib (2017) Rana J, Galib SM (2017) Cataract detection using smartphone. In: 2017 3rd International Conference on Electrical Information and Communication Technology (EICT), IEEE, pp 1–4
  * Ravi and Larochelle (2016) Ravi S, Larochelle H (2016) Optimization as a model for few-shot learning
  * Resnikoff et al. (2012) Resnikoff S, Felch W, Gauthier TM, Spivey B (2012) The number of ophthalmologists in practice and training worldwide: a growing gap despite more than 200 000 practitioners. British Journal of Ophthalmology 96(6):783–787
  * Resnikoff et al. (2020) Resnikoff S, Lansingh VC, Washburn L, Felch W, Gauthier TM, Taylor HR, Eckert K, Parke D, Wiedemann P (2020) Estimated number of ophthalmologists worldwide (international council of ophthalmology update): will we meet the needs? British Journal of Ophthalmology 104(4):588–592, DOI 10.1136/bjophthalmol-2019-314336, URL https://bjo.bmj.com/content/104/4/588, https://bjo.bmj.com/content/104/4/588.full.pdf
  * Selvaraju et al. (2017) Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618–626
  * Shaheen and Tariq (2019) Shaheen I, Tariq A (2019) Survey analysis of automatic detection and grading of cataract using different imaging modalities. In: Applications of Intelligent Technologies in Healthcare, Springer, pp 35–45
  * Shorten and Khoshgoftaar (2019) Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. Journal of big data 6(1):1–48
  * Sivakamasundari et al. (2014) Sivakamasundari J, Kavitha G, Natarajan V, Ramakrishnan S (2014) Proposal of a content based retinal image retrieval system using kirsch template based edge detection. In: 2014 International Conference on Informatics, Electronics & Vision (ICIEV), IEEE, pp 1–5
  * Song et al. (2022) Song H, Kim M, Park D, Shin Y, Lee JG (2022) Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems
  * Song et al. (2016) Song W, Wang P, Zhang X, Wang Q (2016) Semi-supervised learning based on cataract classification and grading. In: 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), IEEE, vol 2, pp 641–646
  * Song et al. (2019) Song W, Cao Y, Qiao Z, Wang Q, Yang JJ (2019) An improved semi-supervised learning method on cataract fundus image classification. In: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), IEEE, vol 2, pp 362–367
  * Sparrow et al. (1986) Sparrow J, Bron A, Brown N, Ayliffe W, Hill A (1986) The oxford clinical cataract classification and grading system. International ophthalmology 9(4):207–225
  * Srivastava et al. (2014) Srivastava R, Gao X, Yin F, Wong DW, Liu J, Cheung CY, Wong TY (2014) Automatic nuclear cataract grading using image gradients. Journal of Medical Imaging 1(1):014502
  * Staal et al. (2004) Staal J, Abràmoff MD, Niemeijer M, Viergever MA, Van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE transactions on medical imaging 23(4):501–509
  * Supriyanti and Ramadhani (2011) Supriyanti R, Ramadhani Y (2011) The achievement of various shapes of specular reflections for cataract screening system based on digital images. In: International Conference on Biomedical Engineering and Technology (ICBET). Kualalumpur, Malaysia
  * Supriyanti and Ramadhani (2012) Supriyanti R, Ramadhani Y (2012) Consideration of iris characteristic for improving cataract screening techniques based on digital image. In: 2nd International Conference on Biomedical Engineering and Technology IPCBEE, IACSIT Press, vol 34
  * Szegedy et al. (2015) Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9
  * Szegedy et al. (2016) Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826
  * Tawfik et al. (2018) Tawfik HR, Birry RA, Saad AA (2018) Early recognition and grading of cataract using a combined log gabor/discrete wavelet transform with ann and svm. International Journal of Computer and Information Engineering 12(12):1038–1043
  * Tham et al. (2022) Tham YC, Goh JHL, Anees A, Lei X, Rim TH, Chee ML, Wang YX, Jonas JB, Thakur S, Teo ZL, et al. (2022) Detecting visually significant cataract using retinal photograph-based deep learning. Nature Aging DOI https://doi.org/10.1038/s43587-022-00171-6
  * Thylefors et al. (2002) Thylefors B, Chylack Jr L, Konyama K, Sasaki K, Sperduto R, Taylor H, West4 S (2002) A simplified cataract grading system the who cataract grading group. Ophthalmic epidemiology 9(2):83–95
  * Ting et al. (2019) Ting DSJ, Ang M, Mehta JS, Ting DSW (2019) Artificial intelligence-assisted telemedicine platform for cataract screening and management: a potential model of care for global eye health
  * Tolstikhin et al. (2021) Tolstikhin IO, Houlsby N, Kolesnikov A, Beyer L, Zhai X, Unterthiner T, Yung J, Steiner A, Keysers D, Uszkoreit J, et al. (2021) Mlp-mixer: An all-mlp architecture for vision. Advances in Neural Information Processing Systems 34
  * Touvron et al. (2021) Touvron H, Bojanowski P, Caron M, Cord M, El-Nouby A, Grave E, Izacard G, Joulin A, Synnaeve G, Verbeek J, et al. (2021) Resmlp: Feedforward networks for image classification with data-efficient training. arXiv preprint arXiv:210503404
  * Tsui and Chang (2007) Tsui PH, Chang CC (2007) Imaging local scatterer concentrations by the nakagami statistical model. Ultrasound in medicine & biology 33(4):608–619
  * Tsui et al. (2007) Tsui PH, Huang CC, Chang CC, Wang SH, Shung KK (2007) Feasibility study of using high-frequency ultrasonic nakagami imaging for characterizing the cataract lens in vitro. Physics in Medicine & Biology 52(21):6413
  * Tsui et al. (2010) Tsui PH, Yeh CK, Liao YY, Chang CC, Kuo WH, Chang KJ, Chen CN (2010) Ultrasonic nakagami imaging: A strategy to visualize the scatterer properties of benign and malignant breast tumors. Ultrasound in Medicine & Biology 36(2):209 – 217, DOI https://doi.org/10.1016/j.ultrasmedbio.2009.10.006, URL http://www.sciencedirect.com/science/article/pii/S0301562909015622
  * Tsui et al. (2016) Tsui PH, Ho MC, Tai DI, Lin YH, Wang CY, Ma HY (2016) Acoustic structure quantification by using ultrasound nakagami imaging for assessing liver fibrosis. Scientific reports 6:33075
  * Vaswani et al. (2017) Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30
  * Vivino et al. (1995) Vivino M, Mahurkar A, Trus B, Lopez M, Datiles M (1995) Quantitative analysis of retroillumination images. Eye 9(1):77
  * Waltman and Kaufman (1970) Waltman SR, Kaufman HE (1970) A new objective slit lamp fluorophotometer. Invest Ophthalmol 9(4):247–249
  * Wang et al. (2017) Wang L, Zhang K, Liu X, Long E, Jiang J, An Y, Zhang J, Liu Z, Lin Z, Li X, et al. (2017) Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images. Scientific reports 7:41545
  * Wang et al. (2018) Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794–7803
  * Werkmeister et al. (2017) Werkmeister RM, Sapeta S, Schmidl D, Garhöfer G, Schmidinger G, dos Santos VA, Aschinger GC, Baumgartner I, Pircher N, Schwarzhans F, et al. (2017) Ultrahigh-resolution oct imaging of the human cornea. Biomedical optics express 8(2):1221–1239
  * West et al. (1988) West SK, Rosenthal F, Newland H, Taylor H (1988) Use of photographic techniques to grade nuclear cataracts. Investigative ophthalmology & visual science 29(1):73–77
  * Wu et al. (2021) Wu H, Lv J, Wang J (2021) Automatic cataract detection with multi-task learning. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp 1–8, DOI 10.1109/IJCNN52387.2021.9533424
  * Wu et al. (2019) Wu X, Huang Y, Liu Z, Lai W, Long E, Zhang K, Jiang J, Lin D, Chen K, Yu T, et al. (2019) Universal artificial intelligence platform for collaborative management of cataracts. British Journal of Ophthalmology 103(11):1553–1560
  * Xiao et al. (2021a) Xiao Z, Zhang X, Higashita R, Chen W, Yuan J, Liu J (2021a) A 3d cnn-based multi-task learning for cataract screening and left and right eye classification on 3d as-oct images. In: 2021 the 3rd International Conference on Intelligent Medicine and Health, pp 1–7
  * Xiao et al. (2021b) Xiao Z, Zhang X, Higashita R, Hu Y, Yuan J, Chen W, Liu J (2021b) Gated channel attention network for cataract classification on as-oct image pp 357–368
  * Xie et al. (2017) Xie S, Girshick R, Dollár P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492–1500
  * Xu et al. (2019a) Xu C, Zhu X, He W, Lu Y, He X, Shang Z, Wu J, Zhang K, Zhang Y, Rong X, et al. (2019a) Fully deep learning for slit-lamp photo based nuclear cataract grading. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 513–521
  * Xu et al. (2010) Xu L, Yang CW, Yang H, Wang S, Shi YY, Song XD (2010) The study of predicting the visual acuity after phacoemulsification according to the blur level of fundus photography 19(2):81–83
  * Xu et al. (2019b) Xu X, Zhang L, Li J, Guan Y, Zhang L (2019b) A hybrid global-local representation cnn model for automatic cataract grading. IEEE journal of biomedical and health informatics
  * Xu et al. (2013) Xu Y, Gao X, Lin S, Wong DWK, Liu J, Xu D, Cheng CY, Cheung CY, Wong TY (2013) Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 468–475
  * Xu et al. (2016) Xu Y, Duan L, Wong DWK, Wong TY, Liu J (2016) Semantic reconstruction-based nuclear cataract grading from slit-lamp lens images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 458–466
  * Yamazaki et al. (2014) Yamazaki N, Kobayashi A, Yokogawa H, Ishibashi Y, Oikawa Y, Tokoro M, Sugiyama K (2014) In vivo imaging of radial keratoneuritis in patients with acanthamoeba keratitis by anterior-segment optical coherence tomography. Ophthalmology 121(11):2153–2158
  * Yang et al. (2016) Yang JJ, Li J, Shen R, Zeng Y, He J, Bi J, Li Y, Zhang Q, Peng L, Wang Q (2016) Exploiting ensemble learning for automatic cataract detection and grading. Computer Methods and Programs in Biomedicine 124:45 – 57, DOI https://doi.org/10.1016/j.cmpb.2015.10.007, URL http://www.sciencedirect.com/science/article/pii/S0169260715002679
  * Yang et al. (2013) Yang M, Yang JJ, Zhang Q, Niu Y, Li J (2013) Classification of retinal image for automatic cataract detection. In: 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013), IEEE, pp 674–679
  * Yin et al. (2018) Yin P, Tan M, Min H, Xu Y, Xu G, Wu Q, Tong Y, Risa H, Liu J (2018) Automatic segmentation of cortex and nucleus in anterior segment oct images. In: Computational Pathology and Ophthalmic Medical Image Analysis, Springer, pp 269–276
  * Yu et al. (2021) Yu W, Luo M, Zhou P, Si C, Zhou Y, Wang X, Feng J, Yan S (2021) Metaformer is actually what you need for vision. arXiv preprint arXiv:211111418
  * Zafar et al. (2018) Zafar R, Sharif M, Yasmin M (2018) A survey on the prevalence of cataract and its accompanying risk factors. Current Medical Imaging Reviews 14(2):251–262
  * Zhang et al. (2019a) Zhang H, Niu K, Xiong Y, Yang W, He Z, Song H (2019a) Automatic cataract grading methods based on deep learning. Computer methods and programs in biomedicine 182:104978
  * Zhang et al. (2018a) Zhang K, Liu X, Liu F, He L, Zhang L, Yang Y, Li W, Wang S, Liu L, Liu Z (2018a) An interpretable and expandable deep learning diagnostic system for multiple ocular diseases: Qualitative study. Journal of Medical Internet Research 20(11)
  * Zhang et al. (2017) Zhang L, Li J, Han H, Liu B, Yang J, Wang Q, et al. (2017) Automatic cataract detection and grading using deep convolutional neural network. In: 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), IEEE, pp 60–65
  * Zhang et al. (2019b) Zhang S, Yan Y, Yin P, Qiu Z, Zhao W, Cao G, Chen W, Yuan J, Higashita R, Wu Q, et al. (2019b) Guided m-net for high-resolution biomedical image segmentation with weak boundaries. In: International Workshop on Ophthalmic Medical Image Analysis, Springer, pp 43–51
  * Zhang and Li (2017) Zhang W, Li H (2017) Lens opacity detection for serious posterior subcapsular cataract. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 55(5):769–779, DOI 10.1007/s11517-016-1554-1
  * Zhang et al. (2018b) Zhang X, Zhou X, Lin M, Sun J (2018b) Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 6848–6856
  * Zhang et al. (2020a) Zhang X, Lv J, Zheng H, Sang Y (2020a) Attention-based multi-model ensemble for automatic cataract detection in b-scan eye ultrasound images. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp 1–10, DOI 10.1109/IJCNN48605.2020.9207696
  * Zhang et al. (2020b) Zhang X, Xiao Z, Risa H, Chen W, Yuan J, Fang J, Hu Y, Liu J (2020b) A novel deep learning method for nuclear cataract classification based on anterior segment optical coherence tomography images. In: IEEE SMC, IEEE
  * Zhang et al. (2022a) Zhang X, Fang J, Xiao Z, Risa H, Chen W, Yuan J, Liu J (2022a) Research on classification algorithms of nuclear cataract based on anterior segment coherence tomography image. Computer Science DOI 10. 11896 / jsjkx. 201100085
  * Zhang et al. (2022b) Zhang X, Xiao Z, Li X, Wu X, Sun H, Yuan J, Risa H, Liu J (2022b) Mixed pyramid attention network for nuclear cataract classification based on anterior segment oct images. Health Information Science and Systems
  * Zhang et al. (2022c) Zhang X, Xiao Z, Risa H, Chen W, Hu Y, Yuan J, Liu J (2022c) Nuclear cataract classification based on multi-region fusion attention network model. Journal of Image and Graphics 27, DOI 10. 11834 / jig. 210735
  * Zhang et al. (2014) Zhang Z, Srivastava R, Liu H, Chen X, Duan L, Wong DWK, Kwoh CK, Wong TY, Liu J (2014) A survey on computer aided diagnosis for ocular diseases. BMC medical informatics and decision making 14(1):80
  * Zhang et al. (2019c) Zhang Z, Fu H, Dai H, Shen J, Pang Y, Shao L (2019c) Et-net: A generic edge-attention guidance network for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 442–450
  * Zheng et al. (2022) Zheng H, He P, Chen W, Zhou M (2022) Mixing and shifting: Exploiting global and local dependencies in vision mlps. arXiv preprint arXiv:220206510
  * Zhong et al. (2019) Zhong JX, Li N, Kong W, Liu S, Li TH, Li G (2019) Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 1237–1246
  * Zhou et al. (2016) Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921–2929
  * Zhou et al. (2019) Zhou Y, Li G, Li H (2019) Automatic cataract classification using deep neural network with discrete state transition. IEEE transactions on medical imaging