# Cervical Glandular Cell Detection from Whole Slide Image with Out-Of-
Distribution Data

Ziquan Wei  Shenghua Cheng  Jing Cai  Shaoqun Zeng  Xiuli Liu  and Zehua Wang
This work is supported by the NSFC projects (grant 61721092), China
Postdoctoral Science Foundation (grant 2021M701320) and the director fund of
the WNLO.Z. Wei and S. Cheng contributed equally to this workCorresponding
author: X. Liu and Z. WangZ. Wei, X. S. Cheng, S. Zeng , and X. Liu are with
Collaborative Innovation Center for Biomedical Engineering, Wuhan National
Laboratory for Optoelectronics-Huazhong University of Science and Technology,
Wuhan, Hubei, China. They are also with Britton Chance Center and MOE Key
Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong
University of Science and Technology, Wuhan, Hubei, China (email:
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>xlliu@mail.hust.edu.cn).J. Cai and Z. Wang are with Department of Obstetrics
and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of
Science and Technology, Wuhan, China (e-mail<EMAIL_ADDRESS>zehuawang@163.net).

###### Abstract

Cervical glandular cell (GC) detection is a key step in computer-aided
diagnosis for cervical adenocarcinomas screening. It is challenging to
accurately recognize GCs in cervical smears in which squamous cells are the
major. Widely existing Out-Of-Distribution (OOD) data in the entire smear
leads decreasing reliability of machine learning system for GC detection.
Although, the State-Of-The-Art (SOTA) deep learning model can outperform
pathologists in preselected regions of interest, the mass False Positive (FP)
prediction with high probability is still unsolved when facing such gigapixel
whole slide image. This paper proposed a novel PolarNet based on the
morphological prior knowledge of GC trying to solve the FP problem via a self-
attention mechanism in eight-neighbor. It estimates the polar orientation of
nucleus of GC. As a plugin module, PolarNet can guide the deep feature and
predicted confidence of general object detection models. In experiments, we
discovered that general models based on four different frameworks can reject
FP in small image set and increase the mean of average precision (mAP) by
$\text{0.007}\sim\text{0.015}$ in average, where the highest exceeds the
recent cervical cell detection model 0.037. By plugging PolarNet, the deployed
C++ program improved by 8.8% on accuracy of top-20 GC detection from external
WSIs, while sacrificing 14.4 s of computational time. Code is available in
https://github.com/Chrisa142857/PolarNet-GCdet.

Cervical Glandular Cell, Computer-aided Diagnosis, Deep Learning, Object
Detection, Whole Slide Image Detection.

##  1 Introduction

Detecting glandular cell (GC) from whole slide image (WSI) can aid
pathologists to screen cervical adenocarcinomas (ADCA) and reduce the labour
cost by computer-aided diagnosis (CAD) [1, 2, 3]. With the proper amount of
annotated data, the well-trained deep learning model can provide top cells
from WSI to help the screening, as a recommending system. The task is
challenging, however, that squamous cell (SC) is the major in cervical smear.
The sparseness of GC is severe. Otherwise, the morphology of GC and SC is
similar while the basic shape of polar pattern of GC is distinct comparing
with SC. Such characteristics are the direct hardship to the data-driven model
performing accurately without the usage of the prior knowledge of GC basic
shape in general models.

Figure 1: Upper figures: Glandular cell (GC) in cervical whole slide image is
relatively sparse, where blue box denotes GC, and orange denotes squamous cell
(SC). Below figures: Inconsistent polarity of SCs that are folded or clustered
is in the left, and illustrations of polar pattern of GC are in the right,
where blue arrow denotes polar orientation.

Although recognition cervical lesion in cytology has been developed since 2016
by using deep learning [4, 5], related works focus on the major object, SC,
but rarely discuss GC, which is minor but important [6]. They researched
cervical cancer detection on small image sets with general deep learning
methods, only few works mentioned the performance of model on external WSI
set. Like Liang et al. [13] tested the detection performance of both SC and GC
with the deep learning model in only preseleted ROIs of external WSI. And
Gupta et al. [8] only reported a rough detection results from external WSIs by
their ROI prediction model. There is no focus on GC in cervical cytology and
no usage of prior knowledge of GC in related works inspire that there is still
room of improvement of GC detection using deep learning. The lack of
evaluations of cell detection from WSI also leads related works are hardly
deployed to the clinical CAD.

More vivid description of this problem is illustrated in Fig. 1, there are
only 17 GCs existed in the WSI shown in the upper. This sparseness leads the
computation of WSI will meet substantial out-of-distribution (OOD) data
including artifacts, single SC, folded SC, and SC cluster. Even though their
basic shape has obvious distinction comparing with GC as shown in the below of
Fig. 1, general deep learning model is hard to distinguish. Those orange boxes
are predicted by the state-of-the-art (SOTA) object detection model, YOLOX
[9]. Related works for cervical cell detection are designed based on more
abstract knowledge, such as the attention and connection between different
scales [10, 11], the mixed information of multiple resolutions [12], the
prototype representation of multiple cell categories [13] , and using time
series information to enhance the cancer detection [14]. Despite atypical GC
is among their objects, their design concepts have not consider the basic
shape of cervical cell. That is hardly helping to reject those false positives
with wrong basic shape.

This paper proposed a new network to enhance GC detection with prior knowledge
of basic shape of GC. We also provide a new experimental setting to show the
practical performance of model. Firstly, a polar attention network (PolarNet)
is designed to quantify the confusing morphology of GC, that is the basic
shape of polar orientation. The network employs a novel self-attention
mechanism in eight-neighbor that enables it to score the salience of polarity
orientations of GC. Pseudo-GCs that are not significantly polar in the outer
test set are controlled by such polarity role. The network is also a deep
learning module that can be plugged into any general object detection model.
Then, two additional test sets of small images from the external WSI
containing different volume of OOD data are obtained by human and well-trained
model, respectively. Furthermore, a elegant deployment of the proposal is
completed and provided as a C++ program.

In GC detection experiments on both three small image sets, the proposed
PolarNet showed effectiveness, and compared with 5 models including four
different frameworks. With the usage of PolarNet, model can reject false
positive (FP) and increase the mean of average precision (mAP) from 0.007 to
0.015 in average, where the highest exceeds the recent cervical cell detection
model 0.037. The deployed program improved by 8.8% on accuracy of top-20 GC
detection from external WSIs ($n=110$), while sacrificing 14.4 s of
computational time.

Briefly, this work proposed a cervical GC detection method to recognize GC
from WSI, it consists of four contributions:

  * •

In Section 4, an OOD data set is provided using a well-trained YOLOX. The data
is obtained from the severe FP predicted by the deep learning model, and is
able to reflect the performance of GC detection from WSIs that are not fully
annotated.

  * •

In Section 3, a novel eight-neighbor self-attention mechanism is proposed to
quantify the polar orientation of GC and to construct the plugin network
PolarNet. It can be used in general detection model whether it is single-stage
or multi-stage to reject severe FP.

  * •

In Section 5, the usage of PolarNet in four different frameworks both shown
significance for GC detection. For clinical application, the proposal also
performs effectiveness to recommend top-20 GCs from WSI.

  * •

The proposed method is deployed as a C++ program. The computational cost of
PolarNet is listed in detail. The program is released online.

##  2 Related Works

###  2.1 Cervical Cell Detection

Earlier, the traditional framework was based on cytological definitions and
completed cell classification by calculating the nuclear-cytoplasmic ratio of
cervical cells, while Tareef et al. To improve the accuracy of subsequent
classification tasks, their convolutional neural network (CNN) models surpass
traditional machine learning algorithms in segmentation accuracy. But then,
Zhang et al. [15] believed that the unavoidable segmentation error would
always lead to a decrease in the classification accuracy of abnormal cells,
and they proposed the DeepPap model for the first time to directly apply the
convolutional neural network to the classification task of cervical cells to
avoid pre-segmentation processing, and performed well on two public datasets,
Herlev 2005 [16] and HEMLBC [17], showing more than 98% accuracy on both
datasets, but their method still compares with traditional algorithms. more
time consuming. Shanthi et al. [18] explored in more detail the classification
accuracy that can be obtained when using CNN for cervical cell classification,
performing cell edge extraction, cell nucleus segmentation, or directly using
the original image without segmentation, although their model is described in
Herlev 2005. The accuracy rate is only 94% $\sim$ 95%, and it does not fully
surpass the previous method, but it has been verified that it is the most
effective to use the original image directly without segmentation. These early
developments and conclusions using deep learning led to a perception of the
potential of deep learning in CAD.

Later, with the development of general deep learning models, many general
models such as VGG [19], GoogLeNet [20], ResNet [21] and Inception [22] pre-
trained and validated by the large general dataset ImageNet [23] appeared.
Wait. Lin et al. [24] proposed that these pre-trained models from general data
can extract general morphological features, and they transferred these pre-
trained models to the cervical cell classification task, using GoogLeNet in
Herlev 2005 to obtain the highest accuracy of 94.5%. In order to further
improve the computational efficiency of CNN in cervical cell classification,
Dong et al. [25] proposed to combine a lightweight convolutional neural
network with artificial features, by adapting the prior knowledge based on
cytology definition to the Inception V3 model [22], and finally achieved over
98% accuracy on the public dataset Herlev 2005 [16].

Because of the irregular distribution of cells on the slide in cervical
cytology images, it is important to predict both the location and class of
cells. Recently, Xiang et al. [26], Liang et al. [27] proposed a cervical cell
detection model earlier based on the single-stage object detection model YOLO
(You Only Look Once) [28], and they stacked additional Inception V3, and FPN
(Feature Pyramid Network) with content-aware function [29], on their private
10-class cervical cell dataset, end-to-end localization, classification and
classification of cervical cells were completed. Prediction size, and the
average accuracy reached 63.4%, although their image pixels are sufficient
($4000\times 3000$), but the number of images is small ($n=12909$) is a
defect. In order to solve the problem of model training when the amount of
data is small, Liang et al. [13] subsequently proposed a comparison detector
based on a double-stage Faster RCNN. On small-scale ($n=7410$) datasets, the
contrastive detectors can achieve significant improvements over the previous
ones. The above are all cell images obtained by traditional production
methods.

In the work of Tan et al. [7], the authors firstly collected more than 16000
LBC (Liquid-Based Cytology) images as training and validation data. Compared
with traditional cervical cytology images, the LBC image has a clearer
background [30]. They independently set 290 ROI (Region Of Interest) images in
the external full slide as the test set to simulate real CAD process. They
obtained decent accuracy after training a Faster RCNN model. The experiment of
this deep learning model on external data provides preliminary feasibility
support for CAD, although the amount of external data is still smaller than
the training and validation data.

Since the cervical cell detection task requires a larger field of view of
image data than the earlier classification and segmentation tasks, and such
public data sets are scarce, the above related object detection research is
carried out on private data. More important, whether it is cell segmentation,
classification, or cell detection, none of the related works have taken the
full calculation of WSI into their experiments.

###  2.2 Detection from Whole Slide Image

The related research extending from local prediction research to WSI
calculation is still limited. Gupta et al. [8] proposed an automatic ROI
identification method for the first time. Although ROI identification can only
obtain a rough localization from the WSI, their work considered all the pixels
for calculating the WSI, and used all the regions on the WSI for model
training, Excellent ROI classification accuracy is achieved on a private
dataset containing 10 WSI images, and this study provides an idea of
automatically preselecting ROIs for calculating local cancerous cells in WSI.

Cervical cell detection with WSI calculation is also used by several WSI
classification researches. Ke et al. [31] tried to use a nucleus segmentation
CNN to guide classification, and then integrated the segmentation results
through hand-designed feature engineering. They reported a fabulous WSI
classification performance but none of the cell segmentation/classification on
external test set. Subsequently, Zhu et al. [32] proposed an integrated
cervical WSI recognition system, which includes 24 object detection CNNs and
other 4 models. To complete the WSI classification task, they also created a
new 24 classes to deal with the confusing cervical cancer subclasses, and
arranged each object detection CNN for each class. Such a system greatly
improves the robustness, showing accuracy and generalization that surpasses
pathologists, but it is conceivable that the computing resources required by
this system are very large. The author reported its speed is 180 s/WSI. Cao et
al. [10] designed a new attention module added to the Faster RCNN, a two-stage
object detection model, and improved the mAP of cell detection by 2.37%
compared with the baseline. It predicts the confidence of object to weight the
features extracted by ResNet to complete WSI classification. None of the
performance of cell detection from external/OOD test set is reported. Cheng et
al. [12] roughly performed cell detection using low-resolution images, and
then performed feature extraction at the location of the object to complete
the WSI classification. Wei et al. [11] used a new lightweight object
detection model to simultaneously perform cell detection and feature
extraction to complete WSI classification.

However, there is rarely a solution and a evaluation when it comes to a
complete, cell-level prediction computing all local images on cervical WSI.
The training of general deep learning requires the experimental data to
conform to the real distribution, but the cervical WSI contains various non-
relevant content, that is so-called OOD data, to determine the lesion. Giving
such data a full annotation is expensive, scarce, and unnecessary. To develop
new method on cell recognition, datasets used in related work are mostly based
on local small images generated by pathologists from pre-selected ROIs on WSI.
Despite the WSI calculation is completed in WSI classification, their
discussion is focus on the classification while the cell detection is just a
step of it. Therefore, the related research hardly mentions the generalization
and reliability problems that their local prediction model will encounter when
calculating the cells of the whole cervical slide, which weakens the
contribution of their work.

##  3 Method

This section describes the proposed PolarNet and how this plugin module is
working on modern deep learning models.

###  3.1 PolarNet

The PolarNet is designed as shown in Fig. 2. In the one hand, it obtains a
polar attention score matrix by computing the self-attention inside the eight-
neighbor of features. On the other hand, it generates a new feature map by
weighting with the attention score of different orientations also inside the
eight-neighbor area.

Figure 2: Structure of the proposed PolarNet, where $C$ means the channel
number of feature maps. $H$ and $W$ are the height and the width of input
feature map, respectively. The dot-product block refers to the Eq. 1 and the
weighting block refers to the Eq. 2.

The PolarNet is designed to compute the last stage of feature maps from the
backbone of a modern model. Thus, we can assume the feature maps are denoted
by $\mathbf{x}\in\mathbb{R}^{C\times H\times W}$, the polar attention score
matrix by $\mathbf{PAS}\in\mathbb{R}^{9\times H\times W}$, and the output
feature maps of the PolarNet by $\mathbf{y}\in\mathbb{R}^{C\times H\times W}$.
Then, outputs of PolarNet can be writen by follows:

$\mathbf{PAS}_{\cdot,i,j}=softmax(\\{\mathbf{Q}_{\cdot,i,j}\odot\mathbf{K}^{T}_{\cdot,nei[n]}\\}_{n=1,2,\dots,9}),$
(1)
$\mathbf{y}_{c,i,j}=norm(\sum_{n=1}^{9}\mathbf{PAS}_{n,i,j}\times(1+\mathbf{V}_{c,nei[n]})),$
(2)

where $i,j$ means the coordinator of $i^{th}$ row and $j^{th}$ column in
feature maps, $c$ means the $c^{th}$ channel of feature maps, $\odot$ means
the operation of dot-product, $nei\in\mathbb{N}^{9\times 2}$ is the index set
of eight-neighbor
$[(i-1,j-1),(i-1,j),(i-1,j+1),(i,j-1),(i,j),(i,j+1),(i+1,j-1),(i+1,j),(i+1,j+1)]$.
$\mathbf{Q}\in\mathbb{R}^{C\times H\times W},\mathbf{K}\in\mathbb{R}^{C\times
H\times W},\mathbf{V}\in\mathbb{R}^{C\times H\times W}$ means the query, the
key, and the value of feature maps, respectively, for computing the self-
attention:

$\mathbf{Q}=Conv^{1}_{1\times 1}(\mathbf{x}),$ (3)
$\mathbf{K}=Conv^{2}_{1\times 1}(\mathbf{x}),$ (4)
$\mathbf{V}=Conv^{3}_{1\times 1}(\mathbf{x}).$ (5)

###  3.2 Framework of GC Detection

In the general object detection task, multiple models are designed by using
different architectures. Such as the anchor-based single-step architecture of
YOLO series [33], the multiple-step Faster RCNN [34] and Cascade RCNN [35],
and the anchor-free FCOS [36]. They are both structured by the backbone and
the head, where the backbone can employ a neck, like a FPN [29] or a PAFPN
[37], to mix multi-scale feature maps.

Particularly, whether using the neck in the backbone or not, the PolarNet is
plugging into the end part. It computes the last stage of feature maps, the
$5^{th}$ stage (downsampling ratio is $2^{5}=32$), to generate $\mathbf{y}$
and $\mathbf{PAS}$. The new feature maps $\mathbf{y}$, then, input to the head
of model to predict bounding boxes of GC.

Figure 3: Polar orientations in the eight-neighbor. (a) Using the mean of
polar attention score to represent the polar salience of GC. (b) Illustrating
orientations in one eight-neighbor of one case of GC.

Figure 4: Glandular cell detection framework with PolarNet, where $H_{in}$,
$W_{in}$ are the height and the width of input image, respectively.

The reason of using the last stage of feature maps is pursuing the best
fitness between the area size of one feature vector and the physical size of
GC. In the PolarNet, polar orientations are split evenly into eight
orientations with a self-referential orientation as shown in Fig. 3. To
properly estimate the true orientation of GC by the PolarNet, one feature
vector of input maps should represent the area of around
$\frac{1}{3}\sim\frac{2}{3}$ length of GC. Downsampling ratio in the $5^{th}$
stage is just hitting this range. Under the resolution of image in this work,
$0.2499\ \mu m/pixel$, one $5^{th}$ stage feature vector represents the
$7.9968\ \mu m$ length area, which is $\frac{1}{3}\sim\frac{4}{5}$ times of
GC, such as the case in Fig. 3 (b). Thus, earlier stages, like $4^{th}$ with
the ratio $2^{4}$ or $3^{th}$ with $2^{3}$, are unsuitable to predict the
polar orientation of GC by the self-attention mechanism of eight-neighbor of
the PolarNet.

Finally, based on the above knowledge, the GC detection framework can be
illustrated as Fig. 4. The confidence of GC bounding box is updated by

$P=(1-\alpha)P_{obj}+\alpha P_{polar},$ (6)

where $\alpha\in[0,1]$ is the weight of polar salience, and $P_{obj}$ means
the objectiveness confidence of original output of model. $P_{polar}$ is the
result by converting $\mathbf{PAS}$ matrix to one scalar

$P_{polar}=\frac{1}{8b_{w}b_{h}}\sum_{i=b_{x}-\frac{b_{w}}{2}}^{b_{x}+\frac{b_{w}}{2}}\sum_{j=b_{y}-\frac{b_{h}}{2}}^{b_{y}+\frac{b_{h}}{2}}\sum_{n}^{1\sim
4,6\sim 9}\mathbf{PAS}_{n,i,j},$ (7)

where $b_{x},b_{y},b_{w},b_{h}$ mean the coordinator and the size of one
bounding box. It is clear that Eq. 7 computes the mean of polar attention
score to represent the polar salience of GC as shown in Fig. 3 (a).

In the training phase, the PolarNet is supervised by cross entropy as similar
as the objectiveness loss in modern object detection model

$L_{PolarNet}=log(NLLLoss([P_{non-polar}\ P_{polar}],P_{gt})),$ (8)

where $P_{gt}$ means the objectiveness ground truth of one bounding box, and
the operation combination of $log$, $NLLLoss$, and $softmax$ in Eq. 1 forms
the cross entropy loss with

$P_{non-
polar}=\frac{1}{b_{w}b_{h}}\sum_{i=b_{x}-\frac{b_{w}}{2}}^{b_{x}+\frac{b_{w}}{2}}\sum_{j=b_{y}-\frac{b_{h}}{2}}^{b_{y}+\frac{b_{h}}{2}}\mathbf{PAS}_{5,i,j}.$
(9)

At this point, any modern model in the object detection task is able to use
the prior knowledge, the ubiquitous polarity of GC, to reject false positives
of GC from WSI.

##  4 Data Preparation

This work uses total 486 cervical cytology WSIs from Tongji Union Hospital,
Huazhong University of Science and Technology. The scanner used a $20\times$
objective lens with a resolution of $0.2499\ \mu m/pixel$, and used Qupath
software [38] to complete the local annotation of cervical GC in WSIs.

According to cervical GCs are more sparse than squamous cells, three different
sources are set up to make the image datasets for full validation and testing.
As shown in Table 1, the three sources are GC annotation, non-relevant content
(NC) annotation and false positive (FP).

Among them, the GC annotation refers to the area containing glandular cells
(clumps) in the cervical slide that is judged to be positive (with the
presence of significant atypical GCs), and has two subclasses: AGC (Atypical
Glandular Cells) and nGEC (normal Glandular Epithelial Cells). Non-relevant
content (NC) annotation refers to some small areas from negative WSIs (no
significant atypical glandular cells are present) that do not contain any GC.
False positive (FP) refers to the wrong predictions in the test set of GC
annotation source by a modern model, YOLOX-l [9]. The source of the first GC
annotation is provided by the pathologist, and the latter two are generated by
authors. All annotations have been reviewed by the pathologist for the double
check.

Table 1: Data set details. source |  | slide num. | image num. | AGC num. | nGEC num.
---|---|---|---|---|---
GC ann. | train | 221 | 8274 | 8759 | 17190
val | 66 | 1032 | 258 | 1331
test | 44 | 779 | 226 | 1364
NC ann. | test | 45 | 1280 | 0 | 0
FP | test | 110 | 3496 | 0 | 0
total |  | 486 | 14861 | 9243 | 19885
Note: num. means number, ann. means annotation, NC means non-relevant content,
FP means false positive by a modern model.

Images listed above are used with the same size, $1024\times 1024$, and the
same original resolution.

  * •

While the images are from GC annotations, first, those GC annotations on a
slide that are close enough (smaller than the size of image) are used as a
set. Then, a larger cell image is cropped from WSI centered on such set with
the size of $1536\times 1536$ and saved in the hard disc. During training,
$1024\times 1024$ regions are randomly cropped from the larger images to
ensure the diversity of learning samples, and $1024\times 1024$ are cropped
from the center for validation and testing.

  * •

While the images are from non-relevant content annotation, $1024\times 1024$
cell images are cropped from WSI, randomly, for testing.

  * •

The false positive source is obtained by a YOLOX-l [9] model that trained with
the first source, GC annotations, in our data. It is from external positive
WSIs ($n=110$) that are entirely inferred by the well-trained YOLOX-l. The top
bounding boxes ranking by the confidence (named as top-$N$ results) of every
WSI are visualized using the Qupath tool, and manually judged one by one.
Results that are not GCs are then cropped in the same way of above sources for
testing. During the manual judgement, all top-100 in the first 30 slides and
20 random results of top-100 in the last 80 slides are reviewed.

Figure 5: Examples of GC used in this work. (a) Image with nGEC annotations
(green boxes). (b) Image containing non-relevant content. (c) Image with false
positives (orange boxes) by the well-trained YOLOX-l.

Figure 6: Precision-recall curves on three test data setting.

Among the results of above manual judgement, it is interesting that the
average accuracy of first 30 is only 0.1445. Although this model is very
popular in the industry, the accuracy of only 2 slides exceeded 0.9 due to the
fact that the cervical GC’s prior knowledge is not taken into the model. It is
worth noting that this phenomenon of external testing on YOLOX-l indicates
that standard experimental results, which shown an average precision of more
than 0.7 on the validation set, are unreliable when facing the sparse GC from
real WSI. Some examples are shown in Fig. 5.

It is worth mentioning that testing with the non-relevant and false sources,
the images without the presence of GCs accounted for 62.17% and 81.78% of the
total, respectively. Therefore, the unreliability of the model can be fully
demonstrated in both. In fact, the proportion of images without GCs in the
whole slide generally exceeds 90%, so testing with those two sources can
simulate the performance of deep learning models for GC detection from WSI.

##  5 Experiments

###  5.1 Evaluation Criteria

First of all, for the GC detection from $1024\times 1024$ image, this work
uses the common evaluation criteria, AP50 (Average Precision with 50% IoU
threshold), and its calculation formula is

$\text{Recall}=\frac{TP}{TP+FN},\text{Precision}=\frac{TP}{TP+FP},$ (10)

where $TP$, $FP$, and $FN$ are true positive, false positive, and false
negative, respectively. Since AP50 is obtained by calculating the area under
the Precision-Recall curve, we also provide the curve to show the GC detection
performance of models in detail.

Second, for the task of GC detection from cervical cytology WSI, this work
uses the top-$N$ accuracy of GC to demonstrate the reliability of models. It
is calculated by $\frac{TP}{2(TP+FP)}+\frac{TN}{2(TN+FN)}$ with $TN$ the true
negative.

###  5.2 Training Environments

All the experiments in this work are implemented with the PyTorch deep
learning library [39] on a Win10 OS computer. Model training is performed
using the SGD optimizer [40], and the learning rate descent strategy is the
common stepwise, from $5\times 10^{-3}$ to $5\times 10^{-6}$, learned at
epochs 25, 50, and 80, respectively. The rate decay is 0.1, and the maximum
training epoch is 100. Memory is 128 Gb, CPU is a Xeon® 6134 @3.20 GHz, GPU is
a Tesla P40. The comparison methods are trained using the code published by
the relevant research, and the general popular model uses the implementation
version of TorchVision model zoo. When using the proposed PolarNet, only one
hyper-parameter $\alpha$ is always set as 0.5.

###  5.3 Comparing Methods

Regarding the comparison methods, since YOLOX and Faster RCNN are
representative single-stage and double-stage methods commonly used in general
tasks. AttnFaster [10], that is specially designed for cervical cytology based
on attention mechanism, is also chosen to test. In addition, more existing
models are compared: one anchor-free model FCOS, and one multi-stage model
Cascade RCNN [35].

###  5.4 GC Detection Results

As mentioned in Section 4, the experimental data in this work were produced by
three different sources, each of which represented different levels of
sparsity and difficulty of GC detection, to verify the effectiveness of models
on real distribution of data from WSI. For fully comparing the different
testing scenarios, this section first shows the test results on all three
sources of data, and then shows the detailed test results of GC, which has two
subclasses.

Table 2: GC Detection AP50. Model Name | val | test | average of test
---|---|---|---
GC | GC | GC+NC | GC+FP
AttnFaster + R50 | 0.570 | 0.438 | 0.319 | 0.238 | 0.332
FCOS + R50 | 0.323 | 0.272 | 0.183 | 0.112 | 0.189
$\sim$ w/ PolarNet | 0.354 | 0.312 | 0.181 | 0.096 | 0.196 (+0.007)
YOLOX-m | 0.592 | 0.358 | 0.315 | 0.275 | 0.316
$\sim$ w/ PolarNet | 0.528 | 0.413 | 0.355 | 0.292 | 0.353 (+0.037)
Faster RCNN + R50 | 0.499 | 0.422 | 0.360 | 0.290 | 0.357
$\sim$ w/ PolarNet | 0.513 | 0.440 | 0.372 | 0.295 |  0.369 (+0.012)
Cascade RCNN + R50 | 0.437 | 0.294 | 0.264 | 0.208 | 0.255
$\sim$ w/ PolarNet | 0.444 | 0.328 | 0.271 | 0.210 | 0.270 (+0.015)

####  5.4.1 Main results

The GC detection results from small images are shown in Table 2. It is obvious
that all SOTA models with PolarNet appear the improvement of AP50 comparing to
their original version. That can show the effectiveness of PolarNet. And,
Faster RCNN with PolarNet always shows the highest AP50 on every test setting
and the average. YOLOX with PolarNet is able to exceed AttnFaster in average
by 0.021, while the original YOLOX is 0.016 lower than AttnFaster.
Interestingly, comparing the results between three different test settings,
the AP50 of only GC source can maintain a similar level of AP50 as the
validation set. But after adding images without target presence (non-relevant
or false positive source), AP50 appears some relatively significant drops.
Even for the most complex Cascade RCNN, which performs best on the general
dataset, the AP50 of test set GC+FP is only 0.255. Due to the lower AP50
scores of FCOS and Cascade RCNN among the general models, we use the more
powerful and more general YOLOX and Faster RCNN for subsequent experiments.

####  5.4.2 Precision-recall curves

The precision-recall (P-R) curves of above models are shown in Fig. 6. It can
be seen that the green of PolarNet used in the P-R curve of the Faster RCNN
model has higher precision than orange and blue, and the red P-R curve of
YOLOX is also lower in precision than adding PolarNet. YOLOX. For Recall, P-R
curves for the same model infrastructure all exhibit similar maximum recall.
While two YOLOXs’ recalls are significantly lower than Faster RCNN, their
precisions are higher when curves start. The relatively low precision at the
beginning of the curve means it will be less effective when encountering
external test sets. This is because the curve is drawn with the results sorted
by confidence, and hence the results with high confidence are less accurate
when that happened. It can be seen that YOLOX after adding PolarNet is
improved at the beginning of the curve.

####  5.4.3 Detailed results

In object detection task, the calculation of AP criteria depends on the
threshold of the intersection over union between bounding boxes of predictions
and annotations. For example, AP50 used above needs to be greater than 50% to
be judged as $TP$. Obviously, a larger threshold can reflect the accurate size
and position of predictions. As shown in Table. 3, in order to demonstrate the
accuracy of sizing and positioning of predictions, we list the results in AP60
and AP70. It can be seen that, similar to the trend in Table. 2, AP60 and AP70
of Faster RCNN with PolarNet still both perform best on test set of the GC+FP
source, while YOLOX with PolarNet ranks third in AP60 and AP70 . Obviously,
compared to AP50, all models show a small drop at AP60 and a significant drop
at AP70. This shows that the accurate detection of cervical GC is still a
challenging problem. Furthermore, AP50 of two subclasses are also shown in
Table. 3. The two models using PolarNet performed the second with 0.112 and
the first with 0.336 in AGC and nGEC, respectively. And, AP50 of AGC is
generally lower than that of nGEC, because the number of AGC annotations in
the training data is less than half of that of nGEC.

Table 3: Detailed results of GC detection. Model Name | nGEC | AGC | AP50 | AP60 | AP70
---|---|---|---|---|---
AttnFaster | 0.267 | 0.079 | 0.238 | 0.168 | 0.077
Faster RCNN | 0.328 | 0.075 | 0.290 | 0.207 | 0.091
$\sim$ w/ PolarNet | 0.336 | 0.083 | 0.295 | 0.210 | 0.093
YOLOX-m | 0.316 | 0.184 | 0.275 | 0.184 | 0.080
$\sim$ w/ PolarNet | 0.325 | 0.112 | 0.292 | 0.190 | 0.078
Note: results of subclasses nGEC and AGC are both AP50.
Table 4: WSI GC Detection Top-N Accuracy in Average. Model Name | $N_{pred}$ | Top-5 | Top-10 | Top-20
---|---|---|---|---
AttnFaster | 20.00 | 0.281$\pm$0.311 | 0.284$\pm$0.274 | 0.271$\pm$0.241
Faster RCNN | 20.00 | 0.424$\pm$0.344 | 0.410$\pm$0.303 | 0.355$\pm$0.261
$\sim$ w/ PolarNet | 20.00 |  0.429$\pm$0.364 |  0.411$\pm$0.344 |  0.386$\pm$0.282
YOLOX-m | 18.89 | 0.117$\pm$0.236 | 0.0.108$\pm$0.212 | 0.122$\pm$0.204
$\sim$ w/ PolarNet | 20.00 | 0.190$\pm$0.283 | 0.205$\pm$0.267 | 0.126$\pm$0.149
Note: $N_{pred}$ means the average number of predictions in every WSI, $\pm$
followed by the standard deviation of accuracies of all WSIs.

###  5.5 WSI GC Detection Top-N Results

When the cervical GC detection model is practically applied to cervical cancer
CAD, the top-$N$ results can guide pathologists to prioritize the screening of
suspicious lesions. Therefore, the top-$N$ results of models on those external
slides ($n=110$) can demonstrate its performance from the perspective of
cervical cancer auxiliary diagnosis on WSI. This section shows the accuracy of
top-$N$ results, with higher accuracy indicating greater potential for the
model to be applied to CAD.

As shown in Table. 4, the accuracy of top-$N$ results when $N=5$, $10$, and
$20$ are listed respectively. It is clear that Faster RCNN with PolarNet
performs the best among the three different $N$. As $N$ decreases, its
accuracy increases from 0.386 to 0.429. This shows that it has a reasonable
confidence distribution, such as higher confidence results have higher
accuracy. In fact, Faster RCNN is the only remaining model with the same
trend, and the other remaining three models show a drop in accuracy when using
higher confidence results. In YOLOX models, its accuracy is much lower than
the other models as shown in Fig. 7. This situation can also be reflected from
$N_{pred}$, the average number of predictions. There are some slides that
cannot give the complete 20 detection results, even though YOLOX used a very
low confidence threshold (0.0001). Although adding PolarNet can alleviate this
situation, the top-$N$ accuracy of YOLOX with PolarNet is still at least about
0.08 lower than the double-stage Faster RCNN. This shows that the double-stage
model is the most reliable when faced with real external WSI comparing with
others.

Figure 7: The box plot showing the distribution of WSI GC detection top-20
results.

###  5.6 Ablation Studies

In order to further reveal the role of PolarNet in object detection models and
prove the effectiveness of polar attention for cervical GC detection, two
ablation studies are performed in this section: 1) The contribution of
$P_{polar}$, the mean of polar attention score, in the confidence $P$ updating
Eq. 6. 2) The effect of feature scale (see Section 3.2) on the estimation of
GC polarity in PolarNet.

####  5.6.1 The contribution of $P_{polar}$

Based on Eq. 6 that changing the value of $\alpha$ can change the contribution
of $P_{polar}$. Especially, when $\alpha=0$, $P_{polar}$ contributes nothing
in the role of PolarNet but only the new feature maps guided by polar
attention. Therefore, observation of the $\alpha$-AP50 curve can
simultaneously demonstrate the effectiveness of $P_{polar}$ and polar
attention matrix.

Fig. 8 shows the $\alpha$-AP50 curve by two types of model. It can be seen
that whether it is a single-stage YOLOX or a multi-stage Faster RCNN, the AP50
raises when $\alpha$ increasing, and reaches a peak after the default setting
$\alpha=0.5$ in this work. This indicates that the polar score provides a
positive contribution to the GC detection results. Moreover, it can be seen
that when $\alpha=0$, the AP50 of the model is still superior to the results
of other models in Table. 2 on the three test settings. This shows that polar
attention also provides an important contribution to the feature
representation of the model.

Figure 8: $\alpha$-AP50 curves to show the significance of $P_{polar}$, the
mean value of polar attention score.

Figure 9: GC detection examples by four models to show the ability of
rejection of false GCs after using PolarNet, where green boxes are ground
truth, oranges are false positives, and blues are true positives.

####  5.6.2 The effect of feature scale

As described in Section. 3.2, since the physical size of a single GC is
relatively fixed, PolarNet calculates its polarity orientation by feature maps
that are sensitive to the scale of GC. This part verifies that the scale,
$2^{5}$, in $5^{th}$ stage that covers about $\frac{1}{3}\sim\frac{4}{5}$ of
the cell size obtains optimal polar attention. We test all available feature
scales for both the single-stage model and the double-stage model, showing the
effect of feature scale on PolarNet.

As shown in Table. 5, the results AP50 of cervical GC detection using
different scales of feature for PolarNet. Obviously, the model using the
largest scale, $2^{5}$, performs the best on all test settings. This shows
that the theoretical foundation for computing polar attention is valid. In
addition, it can be seen that after PolarNet uses smaller scales, most of the
AP50, such as non-bold and non-underlined, are even lower than the
corresponding original model shown in Table. 2. It shows that when PolarNet
uses an inappropriate scale, the polarity calculation via self-attention
mechanism in eight-neighbor no longer has a positive effect.

Table 5: GC Detection AP50 on Test Set using Different Feature Scales. Scales | Faster RCNN w/ PolarNet | YOLOX-m w/ PolarNet
---|---|---
GC | GC+NC | GC+FP | GC | GC+NC | GC+FP
$2^{5}$ | 0.443 | 0.376 | 0.295 | 0.413 | 0.355 | 0.292
$2^{4}$ | 0.382 | 0.321 | 0.256 | 0.436 | 0.324 | 0.222
$2^{3}$ | 0.396 | 0.320 | 0.228 | 0.343 | 0.266 | 0.201
$2^{2}$ | 0.444 | 0.338 | 0.247 | - | - | -

###  5.7 Qualitative Results

Finally, Fig. 9 shows the detection examples of cervical GC for the four
models for comparison, including the detection examples of two groups of
independent GCs and another two of GC clusters. First, in all examples by
every model, it is difficult to avoid giving false GCs (orange box), which is
caused by the sparseness of GC in WSI. Then, compared with Faster RCNN and
YOLOX-m, pulgging PolarNet to modern models can generally reduce the
confidence of false bounding boxes of GC. In these four sets of examples,
except for the Faster RCNN in the second row, all the others have removed
false positives (orange boxes) by PolarNet’s scoring of polar attention, and
in some cases even corrected false negatives, such as the first and second
lines, where new blue box is appearing. These examples visually illustrate the
role of PolarNet in GC detection.

###  5.8 Computational Costs

In additional, this work also complete the C++ deployment of the trained
cervical GC detection model.111C++ program is released in
https://github.com/Chrisa142857/You-Only-Look-Cytopathology-Once/tree/main/cpp
The C++ program is able to uses any modern model to complete the cervical GC
detection from WSI. The only need is to change the model path and set some
hyper-parameters in the command line. To test computational costs, this
section involves 5 object detection models that tested in above experiments:
AttnFaster, Faster RCNN, Faster RCNN with PolarNet, YOLOX-m, and YOLOX-m with
PolarNet.

Table. 6 shows computational costs using the proposed PolarNet in our C++
program in detecting GCs from WSIs. Although the YOLOX model can achieve the
minimum average time of 107.6 s, its accuracy drops severely, only 12.2%. The
best model Faster RCNN with PolarNet improves the accuracy by 3.1% compared
with its original version by sacrificing the time-consuming of 14.4 s in
average.

Table 6: Computational Costs of GC Detection from WSIs ($n=110$). Model Name |  Total Time |  Average Time |  Top-20 Acc.
---|---|---|---
AttnFaster | 6.8 hr | 221.3 s | 27.1%
Faster RCNN | 7.5 hr | 244.3 s | 35.5%
$\sim$ w/ PolarNet | 7.9 hr (+0.4) | 258.7 s (+14.4) |  38.6% (+3.1)
YOLOX-m | 3.3 hr | 107.6 s | 12.2%
$\sim$ w/ PolarNet | 4.6 hr (+1.3) | 151.1 s (+43.5) | 12.6% (+0.4)

##  6 Conclusion

Current cervical cell detection works generally do not consider the morphology
and sparseness of GC in WSI. But the slender shape of GC is easily confused,
which makes the general model have serious false positives when detecting WSI.
In order to improve the reliability of GC detection from WSI, a PolarNet is
designed in this paper. It evaluates the polarity of cells by calculating
eight-neighbor self-attention, and generates polarity score and polarity
attention to guide confidences and feature maps, respectively, to eliminate
pseudo-GC with insignificant polarity. The experimental results show that
PolarNet can improve the Top-20 accuracy of GC detection from WSI by 8.8% on
the presence of sacrificing the computational time per slide of 14.4 s.

##  7 Discussion

In this paper, a novel network PolarNet is proposed, which can effectively
eliminate pseudo-GCs with insignificant polar orientations in the detection
results, and more reliably complete GC detection from WSI. The network obtains
the polar average score in the detection frame by calculating the eight-
neighborhood attention score in the feature map to judge whether the polarity
direction of the cell is significant, and can be added to any general model as
a module. In order to obtain suitable polarity orientations, it uses the
appropriate scale of the $5^{th}$ stage feature map, so that the polarity
orientations can be obtained from general object detection model to cover
$\frac{1}{3}\sim\frac{4}{5}$ shape of a single GC. In experimental sections,
in order to demonstrate the detection performance from WSI, PolarNet was first
tested on three small images with different degrees of sparseness of GC and
showed superior average accuracy, and then top-$N$ accuracy was also shown the
cervical GC detection from WSI. In addition, two ablation studies further
demonstrate the positive effect of polar attention score and the new polar
guided feature maps of PolarNet. In general, the PolarNet designed based on
the prior knowledge of cervical GC can effectively improve the reliability of
GC detection from WSI with OOD data.

However, there is still room to improve the proposal. The current PolarNet
obtains the polar attention score by calculating the feature map at the fixed
scale, which is too sensitive to the change of scale as shown in the second
ablation study. Although the scale of a single cervical WSI is hardly to
change, the network suffers when encountering data from multiple sources.
Therefore, it is very important to further develop scale-invariant PolarNet.

Since the deployment of C++ program is working nice, further clinical
experiments could be carried out to verify the effectiveness in real-world
tasks. The proposed program has a good improvement with a slight time cost
under current computational resources, and its usefulness and weakness can be
further revealed by running to the actual cervical cancer CAD.

## Acknowledgment

The authors want to thank pathologists and organizations that provided the raw
data and the manual annotations. As well as the Collaborative Innovation
Center for Biomedical Engineering and the Britton Chance Center and MOE Key
Laboratory for Biomedical Photonics should be greatly appreciated for their
platforms and devices. This work is supported by the National Natural Science
Foundation of China (NSFC) projects (grant 61721092), China Postdoctoral
Science Foundation (grant 2021M701320) and the director fund of the Wuhan
National Laboratory for Optoelectronics (WNLO).

## References

  * [1] Y. Li, J. Chen, P. Xue, C. Tang, J. Chang, C. Chu, K. Ma, Q. Li, Y. Zheng, and Y. Qiao, “Computer-aided cervical cancer diagnosis using time-lapsed colposcopic images,” _IEEE Transactions on Medical Imaging_ , vol. 39, no. 11, pp. 3403–3415, 2020. [Online]. Available: https://dx.doi.org/10.1109/tmi.2020.2994778
  * [2] A. Pirovano, L. G. Almeida, S. Ladjal, I. Bloch, and S. Berlemont, “Computer-aided diagnosis tool for cervical cancer screening with weakly supervised localization and detection of abnormalities using adaptable and explainable classifier,” _Medical image analysis_ , vol. 73, p. 102167, 2021\.
  * [3] F.-Q. Li, S.-L. Wang, and G.-S. Liu, “A bayesian possibilistic c-means clustering approach for cervical cancer screening,” _Information Sciences_ , vol. 501, pp. 495–510, 2019.
  * [4] A. Pouliakis, E. Karakitsou, N. Margari, P. Bountris, M. Haritou, J. Panayiotides, D. Koutsouris, and P. Karakitsos, “Artificial neural networks as decision support tools in cytopathology: past, present, and future,” _Biomedical engineering and computational biology_ , vol. 7, pp. BECB–S31 601, 2016.
  * [5] D. A. Van Valen, T. Kudo, K. M. Lane, D. N. Macklin, N. T. Quach, M. M. DeFelice, I. Maayan, Y. Tanouchi, E. A. Ashley, and M. W. Covert, “Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments,” _PLoS computational biology_ , vol. 12, no. 11, p. e1005177, 2016.
  * [6] H. Jiang, Y. Zhou, Y. Lin, R. C. Chan, J. Liu, and H. Chen, “Deep learning for computational cytology: A survey,” _arXiv preprint arXiv:2202.05126_ , 2022\.
  * [7] X. Tan, K. Li, J. Zhang, W. Wang, B. Wu, J. Wu, X. Li, and X. Huang, “Automatic model for cervical cancer screening based on convolutional neural network: a retrospective, multicohort, multicenter study,” _Cancer Cell International_ , vol. 21, no. 1, pp. 1–10, 2021.
  * [8] M. Gupta, C. Das, A. Roy, P. Gupta, G. R. Pillai, and K. Patole, “Region of interest identification for cervical cancer images,” in _2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)_. IEEE, 2020, pp. 1293–1296.
  * [9] Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun, “Yolox: Exceeding yolo series in 2021,” _arXiv preprint arXiv:2107.08430_ , 2021.
  * [10] L. Cao, J. Yang, Z. Rong, L. Li, B. Xia, C. You, G. Lou, L. Jiang, C. Du, H. Meng _et al._ , “A novel attention-guided convolutional network for the detection of abnormal cervical cells in cervical cancer screening,” _Medical image analysis_ , vol. 73, p. 102197, 2021.
  * [11] Z. Wei, S. Cheng, X. Liu, and S. Zeng, “An efficient cervical whole slide image analysis framework based on multi-scale semantic and spatial deep features,” _arXiv preprint arXiv:2106.15113_ , 2021.
  * [12] S. Cheng, S. Liu, J. Yu, G. Rao, Y. Xiao, W. Han, W. Zhu, X. Lv, N. Li, J. Cai _et al._ , “Robust whole slide image analysis for cervical cancer screening using deep learning,” _Nature communications_ , vol. 12, no. 1, pp. 1–10, 2021.
  * [13] Y. Liang, Z. Tang, M. Yan, J. Chen, Q. Liu, and Y. Xiang, “Comparison detector for cervical cell/clumps detection in the limited data scenario,” _Neurocomputing_ , vol. 437, pp. 195–205, 2021.
  * [14] C. Zhang, D. Jia, N. Wu, Z. Guo, and H. Ge, “Quantitative detection of cervical cancer based on time series information from smear images,” _Applied Soft Computing_ , vol. 112, p. 107791, 2021.
  * [15] L. Zhang, L. Lu, I. Nogues, R. M. Summers, S. Liu, and J. Yao, “Deeppap: deep convolutional networks for cervical cell classification,” _IEEE journal of biomedical and health informatics_ , vol. 21, no. 6, pp. 1633–1643, 2017.
  * [16] P. Sukumar and R. Gnanamurthy, “Computer aided detection of cervical cancer using pap smear images based on hybrid classifier,” _International Journal of Applied Engineering Research, Research India Publications_ , vol. 10, no. 8, pp. 21 021–21 032, 2015.
  * [17] L. Zhang, H. Kong, C. Ting Chin, S. Liu, X. Fan, T. Wang, and S. Chen, “Automation-assisted cervical cancer screening in manual liquid-based cytology with hematoxylin and eosin staining,” _Cytometry Part A_ , vol. 85, no. 3, pp. 214–230, 2014.
  * [18] S. P B, F. Faruqi, H. K S, and R. Kudva, “Deep convolution neural network for malignancy detection and classification in microscopic uterine cervix cell images,” _Asian Pacific Journal of Cancer Prevention_ , vol. 20, no. 11, pp. 3447–3456, 2019. [Online]. Available: https://dx.doi.org/10.31557/apjcp.2019.20.11.3447
  * [19] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” _arXiv preprint arXiv:1409.1556_ , 2014.
  * [20] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2015, pp. 1–9.
  * [21] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2016, pp. 770–778.
  * [22] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2016, pp. 2818–2826.
  * [23] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in _2009 IEEE conference on computer vision and pattern recognition_. Ieee, 2009, pp. 248–255.
  * [24] H. Lin, Y. Hu, S. Chen, J. Yao, and L. Zhang, “Fine-grained classification of cervical cells using morphological and appearance based convolutional neural networks,” _IEEE Access_ , vol. 7, pp. 71 541–71 549, 2019. [Online]. Available: https://dx.doi.org/10.1109/access.2019.2919390
  * [25] N. Dong, L. Zhao, C. H. Wu, and J. F. Chang, “Inception v3 based cervical cell classification combined with artificially extracted features,” _Applied Soft Computing_ , vol. 93, p. 106311, 2020.
  * [26] Y. Xiang, W. Sun, C. Pan, M. Yan, Z. Yin, and Y. Liang, “A novel automation-assisted cervical cancer reading method based on convolutional neural network,” _Biocybernetics and Biomedical Engineering_ , vol. 40, no. 2, pp. 611–623, 2020.
  * [27] Y. Liang, C. Pan, W. Sun, Q. Liu, and Y. Du, “Global context-aware cervical cell detection with soft scale anchor matching,” _Computer Methods and Programs in Biomedicine_ , vol. 204, p. 106061, 2021. [Online]. Available: https://dx.doi.org/10.1016/j.cmpb.2021.106061
  * [28] J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” _arXiv preprint arXiv:1804. 02767_ , 2018.
  * [29] T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2017, pp. 2117–2125.
  * [30] E. Hussain, L. B. Mahanta, H. Borah, and C. R. Das, “Liquid based-cytology pap smear dataset for automated multi-class diagnosis of pre-cancerous and cervical cancer lesions,” _Data in brief_ , vol. 30, p. 105589, 2020.
  * [31] J. Ke, Y. Shen, Y. Lu, J. Deng, J. D. Wright, Y. Zhang, Q. Huang, D. Wang, N. Jing, X. Liang _et al._ , “Quantitative analysis of abnormalities in gynecologic cytopathology with deep learning,” _Laboratory Investigation_ , vol. 101, no. 4, pp. 513–524, 2021.
  * [32] X. Zhu, X. Li, K. Ong, W. Zhang, W. Li, L. Li, D. Young, Y. Su, B. Shang, L. Peng _et al._ , “Hybrid ai-assistive diagnostic model permits rapid tbs classification of cervical liquid-based thin-layer cell smears,” _Nature communications_ , vol. 12, no. 1, pp. 1–12, 2021.
  * [33] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2016, pp. 779–788.
  * [34] R. Girshick, “Fast r-cnn,” in _Proceedings of the IEEE international conference on computer vision_ , 2015, pp. 1440–1448.
  * [35] Z. Cai and N. Vasconcelos, “Cascade r-cnn: Delving into high quality object detection,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2018, pp. 6154–6162.
  * [36] Z. Tian, C. Shen, H. Chen, and T. He, “Fcos: Fully convolutional one-stage object detection,” in _Proceedings of the IEEE/CVF international conference on computer vision_ , 2019, pp. 9627–9636.
  * [37] S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, “Path aggregation network for instance segmentation,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2018, pp. 8759–8768.
  * [38] P. Bankhead, M. B. Loughrey, J. A. Fernández, Y. Dombrowski, D. G. McArt, P. D. Dunne, S. McQuaid, R. T. Gray, L. J. Murray, H. G. Coleman _et al._ , “Qupath: Open source software for digital pathology image analysis,” _Scientific reports_ , vol. 7, no. 1, pp. 1–7, 2017.
  * [39] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga _et al._ , “Pytorch: An imperative style, high-performance deep learning library,” _Advances in neural information processing systems_ , vol. 32, 2019.
  * [40] L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in _Proceedings of COMPSTAT’2010_. Springer, 2010, pp. 177–186.