# Computational pathology in renal disease: a comprehensive perspective

Manuel Cossio  Dept. of Mathematics and Computer Science
Universitat de Barcelona
Barcelona, Spain
Zürich, Switzerland
<EMAIL_ADDRESS>
###### Abstract

Computational pathology is a field that has complemented various
subspecialties of diagnostic pathology over the last few years. In this
article we briefly analyze the different applications in nephrology. We begin
by providing an overview of the different forms of image production. We
continue by describing the most frequent applications of computer vision
models, the salient features of the different clinical applications, and the
data protection considerations encountered. We finished the development,
delving into the interpretability of these applications, expanding in depth
the three dimensions of this area.

###### Index Terms:

digital pathology, computational pathology, nephrology, renal disease,
brightfield microscopy, immunoflourescence, immunohistochemistry, electron
microscopy, hyperspectral imagery, interpretability, deep transfer learning

##  I Introduction

Thanks to the expanding research in artificial intelligence, especially
computer vision, computational pathology applications have increased
considerably in recent years.

Digital pathology can be defined as the process or set of processes by which
slides with already processed samples are digitized. Digitizing implies
transferring the image that is observed in a light microscope to an image
file[1]. These files are called WSI (Whole Slide Image) and have a
characteristic that makes them essential for use in pathology: they store
different levels of magnification in the same file. This type of configuration
is called a pyramid and it allows to optimize the visualization and
exploration of the image, by allowing macroscopic exploration at very low
levels of magnification and then going to the detail of interest by increasing
the magnification[2, 3, 4]. The WSI can be stored on physical servers located
in the same hospital or can be stored in cloud storage systems. Its storage
has allowed the interconsultation of the same case between several
pathologists from different parts of the world, considerably expanding the
accuracy of the diagnosis in complex cases[5, 6, 4].

It can be said that digital pathology ends with the digitization of the slide
and its storage in the cloud or the physical server[1]. Part of the extension
of the definition of digital pathology, recently includes the remote
consultation of cases between several institutions from different physical
locations in real time[6]. On the other hand, computational pathology includes
all those projects and/or prototypes where WSI images are used as a dataset
and machine learning or artificial vision algorithms are employed[7, 8, 4].
Computational pathology does not include the use of scanners, or microscopes,
or the implementation of molecular biology laboratory procedures. In this
discipline, we already have the images stored, which have the authorization of
the ethics committee of the donor institutions and, if the case warrants, the
informed consent of the patients to whom they belong[9]. Computational
pathology also includes digital modification of WSIs (cropping, alignment,
contrast, brightness, color channels, color normalization, etc.[10]) and
clinical data capture by aligning WSIs with electronic health records[4].

##  II Data workflow

The data workflow in digital pathology can take many forms. However, the main
skeleton is more or less homogeneous in different countries, health networks,
hospitals and laboratories. We can begin to describe it by naming its first
part: data acquisition. As we have already seen in other publications, data
acquisition begins with the capture of the image by the scanner, once the
sample is treated and prepared following the determined protocol (for example,
H&E). That captured image must be temporarily stored and then reviewed by a
qualified professional to determine if the quality is correct for diagnosis
(focus and tissue area). This verification is usually done with the help of
the barcode (linear or 2D) that identifies the sample and attaches it to the
patient’s file. Once the image is verified as correct (without interpreting
the diagnostic), the storage of the image is moved to a permanent physical or
virtual disk. If the disk is physical, the storage server may be located in
the same hospital or in the same laboratory. If the server is virtual, the
host system can be in the same country or in a different country. Now, at the
moment of establishing the diagnosis (interpreting the image), using it for a
virtual consultation (let it be interpreted by another professional
pathologist in another place) or when an algorithm analyzes it autonomously,
the image must be made available[4].

For this, a transmission of the file must be generated and a deposit of the
same (copy) in another physical place or a read-only link can be generated,
without the need for file exchange.

##  III Article structure

In this article, a general review will be made of the current state of
computational pathology in the field of nephrology. For this, the techniques
used to produce images in renal pathology will be described first with their
specific examples. Then, the most common tasks in computational pathology
focused on renal pathology will be delved into. Some important details for
training automation and data protection will be also analyzed. Finally, the
interpretability in the field of images, computational pathology and
computational pathology applied to renal pathology will be shown and discussed
in depth. Therefore, the article will have the following structure:

  1. 1.

Workflow in renal disease

  2. 2.

Light microscopy (LM)

  3. 3.

Immunoflourescence (IF)

  4. 4.

Immunohistochemistry (IHC)

  5. 5.

Electron microscopy (EM)

  6. 6.

Hyperspectral imagery (HI)

  7. 7.

Renal computational pathology

  8. 8.

Frequent use cases in renal computational pathology

  9. 9.

Image aligning

  10. 10.

Data protection in renal computational pathology

  11. 11.

Interpretability

  12. 12.

First dimension: data level interpretability

  13. 13.

Second dimension: classification level interpretability

  14. 14.

Third dimension: model level interpretability

  15. 15.

Final considerations

##  IV Workflow in renal disease

After obtaining the sample by renal biopsy, there are three usual types of
microscopy techniques that are applied to generate pathology images for
diagnosis: light microscopy, immunofluorescence, and electron microscopy.

##  V Light microscopy (LM)

In light microscopy, the tissue sample obtained must first be fixed to
eliminate metabolic activity. In a series of consecutive steps, non-structural
contents such as water will be replaced with various substances. Finally, by
adding a structure that provides rigidity, fine slices will be cut. On these
slices, the staining protocols will be carried out and the dyes will adhere
differentially to the cellular structures. Specifically for renal pathology,
firstly, after obtaining the sample, it must be processed through the
implementation of formaldehyde buffer and paraffin embedding. Subsequently,
fine deparaffinized slices will be obtained and subjected to the following
stains: periodic acid-silver methenamine, Masson’s trichrome, periodic acid-
Schiff, and hematoxylin and eosin[11].

###  V-A Periodic acid-silver methenamine

This technique is one of those used in nephropathology. The periodic acid of
this stain oxidizes the carbohydrates present in the basement membranes of the
sample, producing aldehydes. These aldehydes then reduce the silver to visible
metallic silver[12].

###  V-B Masson’s trichrome

This technique combines, as the name indicates, three colors to differentiate
structures. In this way, the structures can be separated into[13]:

  * •

Light blue: basement membranes and mesangial matrix.

  * •

Dark blue: collagen.

  * •

Rust-colored:

    * –

Finely granulated: cytoplasm of endothelial and mesangial cells.

    * –

Coarsely granulated: cytoplasm of epithelial cells.

  * •

Orange: proteins.

  * •

Red: fibrin.

  * •

Dark brown: nuclei.

###  V-C Periodic acid-Schiff

This technique also makes it possible to clearly distinguish the basement
membranes, which stain bright pink, and the cell nuclei, which stain purple-
blue[12].

###  V-D Hematoxylin and eosin

This technique, the most widespread and common in medical pathology, stains
cell nuclei purple-blue (hematoxylin) and the cytoplasm and basement membranes
pink (eosin)[12].

Figure 1: Examples of different stainings of a Bowman’s capsule in kidney
biopsies. A, renal cortex taken from a patient with acute thrombotic
microangiopathy stained with Periodic acid-Schiff[14]; B, normal renal cortex
stained with Mason’s trichrome[15]; C, normal renal cortex stained with
periodic acid-silver methenamine[15]; D normal renal cortex stained with
hematoxilin and eosin[16].

##  VI Immunoflourescence (IF)

In immunofluorescence, unlike common histology, the stains are antibodies that
adhere to target biomolecules (antigen) identifying a particular tissue
portion. The antibodies contain a fluorophore, which after being incubated
with the target, bind specifically to it. After incubation, the samples are
observed under a fluorescence microscope, which excites the fluorophore with a
laser. The fluorophore, after excitation, will return the energy taken from
the laser in the form of light of a certain wavelength. Finally this light
will be captured by a sensor in the microscope, processed and digitally built
in the form of an image. In the specific case of immunofluorescence applied to
nephropathology, we have two general approaches.

###  VI-A Direct immunoflourescence (DIF)

The most widespread and oldest is DIF, which is performed directly from a
tissue sample prepared for this specific purpose: frozen tissue. It is
important to note that in order to execute this approach, planning must be
carried out in advance of the patient’s biopsy and the necessary precautions
must be taken to avoid the degradation of the sample during handling.

###  VI-B Immunofluorescence on paraffin embedded biopsies (PIF)

Many times it is not possible to have frozen tissue for various reasons.
Firstly, because it could be not established at the time of the biopsy to
carry out a diagnosis by immunofluorescence. Secondly, because the samples
have been stored for a long time and are not suitable for the procedure. Or
thirdly, simply because it is an interconsultation from another hospital where
it is not possible to send a frozen sample by mail[17]. In these cases, it is
possible to resort to carrying out immunofluorescence from a paraffin block.
It is important to note that paraffin induces protein crosslinking in the
sample, which blocks the binding of antibodies to antigens. Therefore, the
paraffin sample must be subjected to a process called antigen retrieval, in
which proteolytic enzymes (trypsin, proteinase K or pronase E) or heat can be
used to disentangle the protein network. Once the antigens have been exposed,
the immunofluorescence protocol can be carried out[18]. Although this
technique began as a backup for moments when frozen tissue was not available,
something crucial was discovered about antigen retrieval. This process
succeeded in unmasking antigens in some particular samples that were negative
in DIF. That is, antigens that were not seen in the direct technique could
only be observed through the previous step in paraffin[19]. In this way, PIF
was established as a technique of additional diagnostic value, in addition to
being a support for case consultations without frozen tissue samples[17, 19].

###  VI-C Antibodies for renal pathology

The usual routine antibodies that are usually implemented in renal pathology
for immunofluorescence are:

  * •

Heavy chains: IgA (Fig. 2A), IgG (Fig. 2C) and IgM.

  * •

Light chains: Kappa (Fig. 2D) and Lambda (Fig. 2B).

  * •

Complement: C3 and C1q.

  * •

Cellular components: Fibrinogen and Albumin.

Figure 2: Examples of DIF and PIF images in kidney biopsies. A, PIF of renal
cortex taken from a patient with IgA nephropathy, with antibodies against
IgA[20]; B, PIF of renal cortex taken from a patient with lambda-type AL
amyloidosis, with antibodies against lambda[20]; C, DIF of renal cortex taken
from a patient with membranous lupus nephritis, with antibodies against
IgG[21]; D, PIF of renal cortex of a patient with kappa-type light chain
proximal tubulopathy, with antibodies against kappa[20].

##  VII Immunohistochemistry (IHC)

In IHC we use the same principle of specific binding between antibodies and
antigens as in immunofluorescence. The difference is that in this procedure,
the antibody is conjugated with an enzyme (for example, peroxidase) that
catalyzes the color change of a substrate. In the specific case of peroxidase,
the primary substrate is colorless and after the reaction, it turns brown[22,
23].

In the specific case of kidney disease, this technique is used to identify
tumor markers. Within the tumors found in nephrectomies we can find renal cell
carcinomas (clear cell, papillary and chromophobes) and also oncocytomas.
Among the markers usually studied with IHC, we find[22, 24, 23]:

  * •

Cytokeratin: identification of mainly renal cell carcinoma.

  * •

Vimentin: identification of mainly renal cell carcinoma, clear cell and
papillary (Fig. 3).

  * •

Transcription factors PAX8 and PAX9: identification of mainly renal cell
carcinoma.

  * •

Renal cell carcinoma marker: identification of mainly renal cell carcinoma.

  * •

CD10: identification of mainly renal cell carcinoma (clear cell and papillary)

  * •

CD9: identification of renal cell carcinoma, chromophobe (Fig. 3).

  * •

Cadherins E and kidney-specific: identification of mainly renal cell carcinoma
(chromophobe ) and oncocytomas.

  * •

Parvalbumin: identification of renal cell carcinoma (chromophobe) and
oncocytomas.

  * •

Claudins 7 and 8: identification of renal cell carcinoma (chromophobe) and
oncocytomas.

  * •

CD117: identification of renal cell carcinoma (chromophobe) and oncocytomas.

  * •

Alpha-Methylacyl Coenzyme A Racemase (AMCAR): identification of renal cell
carcinoma (papillary).

  * •

Transcription factors TFE3 and TFEB: mainly involved in pediatric renal cell
carcinomas. TFE3 associated with a translocation that involves Xp11.2.

  * •

Cathepsin-K: identification of renal cell carcinoma products of
translocations.

  * •

Uroplakin III: identification of renal cell carcinoma of high grade.

  * •

p63: identification of renal cell carcinoma of high grade.

  * •

Thrombomodulin: identification of renal cell carcinoma of high grade.

  * •

GATA3: identification of renal cell carcinoma of high grade.

Figure 3: Examples of IHC in renal cell carcinoma (RCC) to separate clear cell
subtype from chromophobe[25]. A, clear cell RCC with IHC against Vimentin; B
clear cell RCC with IHC against CD9; C, chromophobe RCC with IHC against
Vimentin; D, chromophobe RCC with IHC against CD9.

##  VIII Electron microscopy (EM)

In renal pathology, EM is a necessary partner to diagnose a large number of
cases, where the resolution of optical microscopy is not sufficient. In
different applications of nephropathology, percentages of cases have been
reported where EM adds relevant information for diagnosis (Table I). Of those
case reports where EM was necessary, the mean was 49%. This means that from
the previous publications, which covered from 1997 to 2020, in 50% of the
cases of nephropathology it was necessary to resort to EM to reach the
diagnosis. Within renal pathologies, there is a group of them[26] that require
the resolution of electron microscopy in order to arrive at a diagnosis (Table
II) For example, in cases such as focal segmental glomerulosclerosis, being
able to determine the degree and extent of foot process effacement by EM is
key to distinguishing between a primary or secondary condition ((Table II).
Additionally, of the conditions listed, there are 6 that have one or more
associated genes. These pathologies (Fabry, Nail-Patella, trans-retinein
amyloidosis, fibronectin glomerulopathy, Alport’s and thin basement membrane)
also have confirmation by genetic sequencing that can help determine the
diagnosis without necessarily having to resort to EM. However, to stage the
progress of those pathologies, it is necessary to have the microscopic image.

###  VIII-A Procedure

The general protocol for processing kidney biopsies for EM begins with the
division of the specimen into 0.5 mm portions and then fixation with 3%
glutaraldehyde and osmium tetroxide. Subsequently, the sample must be
dehydrated and embedded with SPI-EPON 812. Once the sample is embedded, slices
must be made with a glass blade microtome. Finally, when the slices are
produced, they must be stained with uranyl acetate and lead citrate, which,
due to their density, will absorb the electrons to a greater extent. According
to the type of cells and their content, the citrates will be absorbed
differentially, resulting in zones with different concentration (Fig. 4).
Among these zones, we are going to have ones with more citrate that will
absorb more electrons (darker) and others with less citrate (lighter)[27, 28,
11].

Figure 4: Examples of electron microscopy (EM) images of renal biopsies. A, renal biopsy observed with EM showing zebra bodies of a Fabry disease patient[29]; B, renal biopsy observed with EM showing the enlarged and split glomerular basement membrane (GBM) of an Alport’s syndrome patient[30]. TABLE I: List of publications with the percentage of cases where it was necessary to resort to electron microscopy (EM) for diagnosis in nephropathology. Application | % of cases | Total cases | Location | Date | Reference
---|---|---|---|---|---
Kidney transplant | 50 | N/A | Louisiana, USA | 1997 | [31, 32, 33]
Chronic renal allograft rejection | 50 | 91 | Szeged, Hungary | 2001 | [34]
General nephropathy | 72 | 133 | Turku, Finland | 2003 | [35]
Glomerular disease | 38 | 52 | Tunis, Tunisia | 2006 | [36]
Glomerular disease | 20 | N/A | Minnesota, USA | 2006 | [37]
Kidney disease after liver transplant | 80 | 81 | New York, USA | 2010 | [38]
General nephropathy | 39 | 273 | Jeddah, Saudi Arabia | 2011 | [26]
General nephropathy | 31 | N/A | Kathmandu, Nepal | 2013 | [39]
Pediatric nephropathy | 68 | 855 | Peking, China | 2020 | [11]
TABLE II: List of renal conditions that require electron microscopy (EM) to be diagnosed. GBM, glomerular basement membrane; BM, basement membrane; FP, foot process; TM, tubules membrane.  Condition | Gene | Microscopic observation | Reference
---|---|---|---
Fabry | GLA | Zebra bodies / Myelin figures | [40]
Nail-Patella | LMX1B | Zebra bodies / Myelin figures | [41, 42]
Trans retinein amyloidosis | TTR | Amyloid deposits | [43]
Light chain amyloidosis | NO | Amyloid deposits | [43]
Heavy chain amyloidosis | NO | Amyloid deposits | [43]
Serum amyloid A amyloidosis | NO | Amyloid deposits | [43]
Fibrillary glomerulonephritis | NO | Non-branching fibrillary deposits | [44]
Fibronectin glomerulopathy | FN1 | Granular and fibrillary deposits | [45]
Collagenofibrotic glomerulopathy | NO | Deposits of collagen bundles | [46]
Alports | COL4A3, COL4A4 | Basket-weave pattern | [47, 48]
Thin basement membrane | COL4A3, COL4A4 | Diffuse thinning of the GBM | [49]
Diabetic nephropathy | NO | Diffuse thickening of GBM, nonatrophic TM thickened | [50]
Dense deposit disease | NO | Dense transformation of the BM lamina densa | [51]
Minimal change disease | NO | Foot process effacement. | [52]
Focal segmental glomerulosclerosis primary | NO | Extensive foot process effacement | [53]
Focal segmental glomerulosclerosis secondary | NO | Imperceptible FP effacement | [53]
Podocyte infolding glomerulopathy | NO | Intramembranous microspherules, Invagination of FP into GBM | [54]
Transplant glomerulopathy | NO | GBM reduplication | [55]

##  IX Hyperspectral imagery (HI)

This method consists of capturing multiple planes of information from the
tissue sample. The human eye is capable of viewing objects in a wavelength
range of 380 to 740 nanometers (nm). Hyperspectral imagery can extend this
range to 2500 nm[56]. In this way, the sensor will take multiple snapshots of
the same sample at different wavelengths (for example, 400, 450, 500 and 600
nm). This will generate several arrays for each wavelength (Fig. 5,
representation of the left). Consequently, each pixel will have information on
all the wavelength planes that were recorded. So each pixel will have its own
spectrum graph also called spectral signature (Fig. 5, graph on the right)).
The spectral signature is simply the representation of the intensity and
wavelength recorded in each capture[56, 57]. This method has been applied in
the field of pathology, since healthy tissue and pathological tissue present
different spectral signatures[58, 57]. This difference is the key to detecting
pathological tissue, where a bright field image is not capable of providing
information. With regard to applications in renal pathology, thanks to
hyperspectral imaging it has been possible to separate and distinguish two
membranous nephropathies. In these two highly similar pathologies, one is
caused by a virus (hepatitis B virus) and the other is idiopathic[57].

Figure 5: Representation of hyperspectral imagery on kidney tissue. Each color
represents a wavelenght and each pixel of the image contains a full spectrum.

###  IX-A Projection transformation

Since the data of a hyperspectral image is multidimensional, it is advisable
to be able to use dimensionality reduction techniques that also help reduce
noise. Among the most used we can find the principal component analysis (PCA)
or Fisher’s linear discriminant analysis (LDA). These techniques have their
negative counterpart, which lies in the need for the data to have a Gaussian
distribution. Therefore, there is another technique called local Fisher’s
discriminant analysis(LFDA)[57], developed after the previous ones and that
does not need a Gaussian distribution to operate.

##  X Renal computational pathology

In the previous sections we have seen very briefly the different techniques of
histology and image acquisition to work in renal pathology. In the next
sections, we will inquire about the state of research in computational
pathology applied to nephrology.

###  X-A Common tasks for renal pathology

After a review of the literature, we can agree with the position of the
excellent article developed by Huo _et al_ in 2021[59]. The main tasks in
which computer vision algorithms are used in renal pathology are:

####  X-A1 Classification

when we refer to classification in computer vision, we mean to assign a label
to an image. In this way, an image enters the algorithm and the algorithm
returns a label with a probability (Fig. 6). Given that in pathology, whole
slide images (WSIs) have a considerable size, it is customary to partition
them into patches. All patches that correspond to a WSI will then be entered
into the algorithm. The algorithm will set one label per patch. Then, with all
the labels, a heatmap can be made as a visual aid on top of the WSI. A general
label of the WSI can also be established (for example, if we identify patches
with cancer, the entire WSI will correspond to a cancer patient)[60, 59].

####  X-A2 Detection

the main difference between classification and detection is that in
classification we get only one label in the entire patch. It doesn’t matter if
inside this patch there are also other things of interest. However, in
detection, in addition to being able to determine several regions of interest
in the same area, we can also determine the position. This is especially
important in renal pathology, to identify functional units (such as the
glomerulus) or to identify anatomical parts (arteries, capillaries or
membranes). Within the most used techniques for detection, we find the
bounding box(Fig. 6). This technique consists of the creation of an artificial
rectangle that identifies the object of interest (for example, the
glomerulus[61]) in an image. In this way, the input is the same as in the
classification: an image. However the output is different: in detection the
output is the image plus the rectangle where the region of interest is[60,
59].

####  X-A3 Segmentation

when we allude to segmentation, we are alluding to the process of assigning
each pixel of an image a particular label. Thanks to knowing exactly how many
pixels we have with a certain label, we can perform quantification tasks
within an image. For example, in certain neurological diseases it is possible
to measure the area within a certain image that is affected and thus determine
the diagnosis[62]. In pathology, segmentation is basically used to separate
healthy tissue from pathological tissue or to identify functional units (such
as the glomerulus)[60]. The segmentation input is an image and the output is a
mask(Fig. 6). The mask is superimposable on the image, so it indicates the
pixels of interestcite[60, 59].

####  X-A4 Synthesis

unlike the other three approaches, image synthesis produces new images. To
produce these new images, we must first train on the model images. The
generator model will learn from the distribution of the training images and
will generate new images very similar to the training ones(Fig. 6). However,
another model, called a discriminator, will try to discover which are the
artificial images within a batch of artificial and real images[63]. This
pairing between generator and discriminator will serve to produce in each
cycle, artificial images more and more similar to the real ones. In renal
pathology, synthesis is used to produce artificial stains. In this way, the
network is trained so that it can generate a stain from an image with another
stain (for example, H&E to PAS)[60, 59].

Figure 6: Representation of common tasks in computer vision applied to renal
pathology.

##  XI Frequent use cases in renal computational pathology

After our review of the literature, we can structure and detail some frequent
uses of algorithms in renal pathology.

###  XI-A Glomeruli identification

Of the many research works and prototypes analyzed, there is a large group of
them that focus on the identification of glomeruli[61, 64, 65, 66, 67, 68, 69,
70, 71, 72, 73, 74, 75, 76, 77, 78, 79]. This identification can be developed
by segmentation[65, 66, 67, 68, 69, 71, 76, 78] or detection[61, 64, 72, 73,
74, 75, 77, 79]. After the recognition of this functional unit, it is possible
to proceed to separate pathological from healthy. Also, after this separation,
the number of healthy and pathological glomeruli can be quantified, to stage
the patient. Of the stains used in these prototypes, we find the most
frequent, H&E[61, 64, 66, 70, 71, 72, 77, 79], and also the others typical of
renal pathology: PAS[64, 66, 67, 68, 69, 71, 72, 74, 76, 78, 79], PAM[66, 73],
Masson[65, 66, 71, 79], Silver[71, 79] and IF[78]. The mean number of patients
in these studies was 182, while the study with the largest number had 1281[73]
and the one with the least number 10[61]. These prototypes used fliping,
rotation, cropping, scaling, contrast and brightness changes, noise adding,
transposition and HUE color space transition as data augmentation techniques.
Among the most frequent applications of the prototypes we find renal
transplantation[61, 67, 69, 75] and the identification of
glomerulosclerosis[70, 76, 77].

###  XI-B Lesion identification

Following a similar reasoning to the one exposed in the previous section, some
prototypes focused on the detection of lesions in renal tissue[80, 81, 82,
83]. The most frequent pathology found in this group of studies was IgA
nephropathy[80, 82]. Most of the prototypes used PAS-stained[80, 81, 83]
images and only one used IF[82].

###  XI-C Cancer cells identification

Like most of the research papers in oncological digital pathology, a group of
the listed papers focused specifically on the identification of tumor cells in
renal tissue. Of this group, all were performed in renal cell carcinoma with
H&E staining[84, 85, 86, 87]. Two of these works were carried out with images
extracted directly from The Cancer Genome Atlas (TCGA)[84, 85].

###  XI-D Condition identification

Part of the research work was also focused on the identification of specific
medical conditions. The techniques used were EM[88], PAS/PAM[89] and IF[90].
One article in particular[88] focused on a group of pathologies such as
amyloidosis, diabetic glomerulosclerosis, membranous nephropathy, membrano-
proliferative glomerulonephritis and thin basement membrane disease.

###  XI-E Stain generation

As we mentioned before (Section X-A4), the possibility of generating virtual
stains is of vital importance to extend datasets. Two research papers focused
on this process. The first of them generating images of Masson trichrome,
Jones and PAS from a training with H&E images for the diagnosis of non-
neoplastic kidney diseases[91]. Second, IF images are generated from
brightfield PAS images, computationally identifying cellular substructures
such as cell nuclei, collagen, fibrosis, distal tubules, proximal tubules,
endothelial cells and leukocytes[92].

###  XI-F Individual entities identification

With respect to the identification of individual pathological or functional
entities, we have several articles of this type. Among them we find the
identification of fibrosis[74], macrophages and lymphocytes[93], deposits[57],
peritubular capillaries[94], restricted vascularity[95] and deposits in
glomeruli[96]. Other articles instead resort to the segmentation of structures
to produce a more accurate pixel map. In this group we find podocyte
segmentation[97] and tubule segmentation[98]. Additionally, most of the
prototypes that worked on the identification of deposits used some type of
immunological technique (IHC or IF)[93, 98, 94, 95]. Also, the only peer-
reviewed publication that includes the use of hyperspectral imaging was
performed on deposits identification for membranous nephropathy screening[57].

##  XII Image aligning

One of the biggest challenges in optimizing image labeling work is aligning
samples of the same tissue. When performing multiple stains on successive
slices of the same surgical specimen, being able to enlarge the image in the
same position on all stains is key. Since the images that come from the same
surgical piece represent different planes of it, a degree of similarity is
expected. However, given the change in plane and the tissue’s own deformation,
the differences can be widened. For example, if we are observing a glomerulus
in H&E and we want to see if the basement membrane is modified, we can observe
that in the PAS image of the same gromerulus. To do this, we would have to
locate the gromerulus in the PAS image, which will surely be in another tissue
cut plane. There is a technique specifically developed for renal tissue images
that allows aligning images of the same surgical piece subjected to successive
staining. This technique is called image registration[79]. It consists of
grouping images that come from the same origin (surgical piece) and spatially
distributing affine transform matrices throughout the extension of each image.
These matrices are basically recognition points (tissue characteristics) that
are repeated in all the images (Fig. 7). When they have all been identified,
they are concatenated in all the images[79]. The recognition points identified
above are coordinates. Each coordinate has a unique code. This allows that
when an important point is identified in one image (glomerulus in H&E), the
same position can be quickly found in another image (glomerulus in PAS).

Figure 7: Representation of the image registration process proposed by [79].
Image A, is the first image with one stain. Image B, is the second image with
a different stain that come from the same surgical piece. Each blue point
represents a coordinate and the blue lines concatenate corresponding points.
Yellow lines represent the axis of each image.

##  XIII Data protection in renal computational pathology

###  XIII-A Informed consent

Regarding data protection, many articles submitted the justification for the
study to the ethics committee of the institution that provided the data
(following the Helsinki declaration). The committee itself authorized the
execution of the study. It also established the waiver of informed consent
justifying that the study was retrospective and does not involve any further
tests or treatments of the participants (the samples were already present in
the biobank)[80, 74, 75]. Other articles used databases that had already
submitted their donors to informed consent for the use of their data. In this
way, the authors, as long as they are compliant with the conditions of use of
the information and the protection regulations, should not resort to new
consents[84, 85, 71]. Some works did not mention in the text of the article or
in any appendix if they submitted the study to an ethics committee or if they
received an informed consent waiver to proceed with the protocols. It is
possible that the information is available in another link or section, which
is not directly visible to users[69, 70]. Other studies mention directly in
the final statement that they have followed the ethical standards of the
Helsinki declaration. However, they do not mention having submitted the
protocol to validation by the ethics committee or having had any type of
content review by any authority of the partner institution[88].

##  XIV Interpretability

Interpretability of an algorithm can be defined as a set of protocols,
techniques or properties that make the output generation of that algorithm
understandable by humans. When we refer to understandable, we mean that it can
be inferred, for example, what characteristics of an image were those that
defined its belonging to a category (label) in a classification[99].

###  XIV-A Dimensions of interpretability

It has been proposed that three dimensions could be considered when attempting
to interpret the output of an algorithm[99, 100]:

  * •

The first dimension focuses on data. Try to apply techniques to understand
which of the dimensions of our data are the most important to solve the task
at hand. As we mentioned before (Section XV), the reduction of dimensions and
the identification of variables that are correlated can help considerably to
select the best features for our model. Having an insight into what features
are involved in the task helps us partially understand how a decision is
reached.

  * •

The second dimension has to do with prediction. Specifically, how a certain
input resulted in a certain output in the algorithm. For example, in an image
what angles, silhouettes, shapes or colors are necessary for that image to be
classified with a particular label. Examples of this dimension could be CAM
(Section XVI-A), GRAD-CAM (Section XVI-B), ablation GRAD-CAM (Section XVI-C)
and score CAM (Section XVI-D).

  * •

The third dimension has to do directly with the model. What parts of the model
interact with each other and with what force so that the input that enters is
processed to a respective output. In the specific case of neural networks, how
the layers interact with each other and with what weight the information from
a previous layer passes to a later one. Examples of this dimension could be
considered activation maximization (Section XVII-1), feature visualization
(Section XVII-2) and network dissection (Section XVII-3).

##  XV First dimension: data level interpretability

When we work with pathology images (WSI), we subject our hardware to a large
impact on processing time and number of active units. Therefore, reducing the
dimensions of our input data is key to:

  * •

reduce physical storage capacity

  * •

reduce processing time

  * •

increase response speed

When we talk about reducing the dimensions of our data, we are specifically
referring to isolating the key information for the task we need to develop.
That is, all that part of the information that would not contribute to the
task for which the algorithm was built (noise), should disappear. This
separation seems simple in words, but it is much more complex in
practice[101]. We have several strategies to reduce the dimensions of our
data, and we will detail them below.

###  XV-A Principal component analysis (PCA)

To perform this technique on our features, we must first project the values of
those features (for each sample of our dataset) in a space of lower dimension.
The type of projection must ensure that the variance of the data is the
largest. Having the projected data, we will proceed to search for the main
components. These components are vectors that best fit the data. Once we find
these small vectors, they will be the components that will represent the
thousands of values that previously belonged to these variables[101, 102].

###  XV-B Gaussian random projection (GRP)

We can also resort to GRP, which simply consists of projecting our features in
a space of reduced dimensions. To do this we simply multiply our matrix
(features, n x m) by a smaller dimension matrix of random values (m x p) and a
scalar (1/square root p). This scalar will help absorb the impact of the
pairwise distance resulting from the dimension reduction[101].

###  XV-C High correlation feature elimination

This method is based on the precept that groups of variables that have a high
correlation among them can be represented by only one of the group. In this
way, the correlation of each feature with each of its peers must be
calculated. If the correlation is greater than a threshold, then one can be
kept and the other eliminated. The higher the threshold, the less number of
features will be discarded. Therefore, it is important to always perform a
finetuning with different thresholds to find the optimal one[101].

###  XV-D Neural compression

This procedure consists of using the encoder to generate embedded vectors. The
encoder is fed with the images whose dimensions we want to reduce. The result
will be a vector of reduced dimensions for each image. For WSI, there is a
protocol by which the image is divided into patches. Then each patch is
converted to a vector. When all the patches have been converted, the image is
reassembled with the vectors preserving the location of each one[103, 4].

###  XV-E t-distributed stochastic neighbor embedding (t-SNE)

t-SNE is a very useful technique for reducing dimensions of multidimensional
data. This technique uses the abstract representations (feature vectors) of
the last layer of a neural network (before running softmax). Since these
vectors are highly multidimensional, this technique reduces the dimensions in
a non-linear way. After reduction, it produces a representation of the vectors
according to their similarity. This can help us to know, in a multiclass
classification, which are the most important features of each class and how
similar they are with respect to other classes.

###  XV-F Applications in renal computational pathology

Of the dimensionality reduction techniques, we find some applications in
digital pathology. One group managed to locate the most important features for
the classification of different types of nervous system neoplasms, using
images similar to those of renal pathology. Moreover, with the t-SNE they
managed to represent them visualizing the different types of tumors by
similarity[104]. Image compression was very regularly used as a method for
dimensionality reduction of pathology images, in breast cancer[103] and lung
cancer[105]. H&E images were used in the same fashion as many renal pathology
studies. PCA was also used as a color separator in patches of glomeruli,
eliminating noise and isolating structural components more effectively than
the original image[106]. Regarding the correlation of features, a variation of
the method explained above was used to select features correlated with the
pathological class. In this way, the features with a correlation coefficient
greater than a threshold for the pathological class were selected[107].

##  XVI Second dimension: classification level interpretability

###  XVI-A Class Activation Mapping (CAM)

The CAM technique uses the feature maps of the last layer of the neural
network. To generate a single usable result, all feature maps are linearly
combined using the weights learned from the last layer as a guide. In this
way, each feature map is multiplied by its weight and then the linear
combination of all of them is produced. An important disadvantage of this
technique is the inability to operate with fully connected layers in the last
layer. Therefore, the networks that have this characteristic will have to
transform their last layer to convolutional and retrain[108].

###  XVI-B Gradient-weighted Class Activation Mapping (Grad-CAM)

This technique was built using some resources of the previous one. Gradients
backpropagating from the output are computed for each class. The weights are
calculated from the gradient, using global average pooling. These weights
represent, like the previous technique, the importance of each feature map.
However, after linearly combining these feature maps with their weights, ReLU
is applied. This is the main difference. When applying ReLU, the features that
only have a positive influence on the interest class are selected[109, 110].

###  XVI-C Ablation CAM

Unlike the previous technique, in this one the calculation of the gradient is
not made use of. Instead, a process called ablation is employed. To do this,
the values of each feature map are set to zero per iteration. The drop in
activation score is calculated for the given class, for each of the feature
maps. The magnitude of the drop represents the relative importance of each
feature map. Finally, following a protocol similar to the previous one, all
the calculated importances are combined and ReLU is applied to prioritize only
those with a positive effect[111].

###  XVI-D Score CAM

This technique, like the last one, gets rid of the calculation of the
gradient. In this case, as a first step, they generate the feature maps of one
of the final layers. As a second step, with these maps, masks are built and
applied to the original input. Each new image (mask on input) is passed
through the network again. Each pass will generate a score. Finally, all the
generated feature maps will be linearly combined (first step) using the scores
as weights (second step). ReLU is used at the end of the combination, to take
only the positive influence[112].

###  XVI-E Applications in renal computational pathology

Specifically concerning renal pathology, the most widely used interpretability
techniques have been CAM and GradCAM[74, 113]. Regarding the first, it has
been used both at the global level of WSI[74] and at the patch level, with a
focus on the gromerulus. When multiple pathologies are classified at
glomerular level, the focus of the neural network can be seen in the patch.
For example, in patients with amyloidosis, the network can be seen focusing on
an area of the glomerulus where amyloid deposits are placed[113]. Regarding
the technique score CAM, we found only one study that compared its use with
respect to GradCAM in the evaluation of renal tissue in patients with
immunoglobulin A nephropathy[114]. We can see in the images of the heatmaps,
slight differences between one technique and another for the same image and
the same class. Regarding dark field images, GradCAM has been applied as well.
It has been found that the technique is equally useful and that it is able to
point out the regions of importance in the classification, even in digitally
blurred images[82].

##  XVII Third dimension: model level interpretability

####  XVII-1 Activation maximization

This method aims to find the parts of a certain input (for example, parts of
an image) that the network considers to be the most important in order to
assign a certain output to that input. In this way, once the model has been
trained, this method finds all the features that make up a class. For example,
if we train our model with numbers from MNIST, we can generate a list of
representations that characterize the label ’8’. This list would contain upper
and lower curves that would filter out images resembling a number 8[115]. As
we continue with a very particular field, without direct examples in renal
computational pathology, we will analyze an example of activation maximization
in brain pathology. In this specific case, the filters of layer 159 that most
highly activated the neurons for each class were visualized. When analyzing
the filters, features were found that corresponded to the type of morphology
found[116].

####  XVII-2 Feature visualization

This research area encompasses a set of techniques and protocols for
visualizing features in neural networks. There are a variety of approaches,
the main one being optimization to visualize features that can be at the
neuron level, at the channel level or at the layer level. Thanks to the
optimization, the differences between instances can be interpreted so that two
neurons fire together or separately. However, a great challenge in this area,
and which extends to all fields of neural networks, is the separation of noise
from task-competent information. Especially in feature visualization, we can
find representations that are basically noise but that cause neurons to fire
in a pattern similar to another image. Being able to identify this and
generate representations that make sense is key to being able to interpret an
output[117].

####  XVII-3 Network dissection

This method basically focuses on understanding the behavior of each layer of a
neural network. A dataset is used that has different images where each pixel
has a particular label (a mask). For example, if we have an image of a garden,
all the pixels that correspond to a flower will have a value of 1 and the rest
will have a value of 0. Then the activation map of the layer that we want to
study is extracted, taking into account that since it is smaller than the
input image, we will have to resize it with bilinear interpolation. When we
have the activation map with the same size as the input, we compare it with
the mask that we generated first. This way we will know what that layer is
focusing on. For example, if we have an image with 3 objects (a plate, a glass
and a fork), we will have 3 masks. The plate label mask will have values of 1
in all pixels that overlap with the plate. In this way, when we pass the image
through the network and obtain the activation map, if it matches better with
the plate mask, we will know what is important for the network[118].

###  XVII-A Applications in renal computational pathology

Given that the third dimension of interpretability is a relatively modern
field and its application tells us more about the model than about the model’s
task, there are few works applied to Renal computational pathology. One of the
works has delved into the features that the model sees when it performs a
classification. In addition to having the map of what the model observes, a
descriptor has also been incorporated that allows generating a text output on
the region of interest (ROI)[119]. The images used have been of H&E histology
for urothelial cancer, with a color quite similar to the tumor images of renal
samples.

##  XVIII Final considerations

In the different sections of this article we briefly saw the different
partners of renal computational pathology. Starting with the methods for
generating images from tissues, we have briefly explored each of them. We have
observed the importance of electron microscopy in the diagnosis of renal
pathologies. Likewise, we have investigated a relatively new technique such as
hyperspectral imaging to further refine the diagnosis of certain pathologies.

In advancing computational pathology methods in nephrology, we have classified
the field of computer vision applications. From that classification, we have
briefly explored the field of what is published in the area. We have provided
an analysis that includes the medical conditions studied, the algorithms, the
number of patients and the data augmentation techniques, among other important
parameters. We have observed that most of the applications identify primarily
the glomerulus. They continue from there in the analysis of that functional
unit to detect pathology. Additionally, we have witnessed the possibility of
generating new stainings from H&E images using generative adversarial networks
(GAN).

As an operational part of the pipelines for computational pathology, we have
investigated the techniques for aligning consecutive images (multiple stains)
of the same surgical piece. This is extremely useful to speed up the
identification of structures in different techniques. For example, correlate
IHC markers on H&E images and generate normal and pathological patches.

We have also briefly analyzed the different data protection constraints that
different works considered when operating with sensitive patient information.
In this particular section we have observed that there is no homogeneous
protocol to be followed globally and that many times it is the authors
themselves who decide not to submit the research protocol to the review of an
institutional ethics committee.

Finally, we have delved into the dimensions of interpretability and its
importance in translating the outputs of our models into clinical medical
language. Of these dimensions, we have seen how there is a lack of
representativeness of the third dimension (model behaviour) not only in renal
computational pathology but also in the general computational pathology of the
third dimension. Although applications have been exposed in the general field
of computer vision, we have found very few works in computational pathology.
This could represent an interesting field of research in the future, since it
would propose new hypotheses to illuminate the black box that many models
represent.

## Acknowledgment

The author would like to thank Ramiro Gilardino, MD for reviewing the
manuscript.

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In this appendix we include a table with a brief cataloging and analysis of
the relevant publications in Renal computational pathology. The analysis
extracts the objective (use) of the algorithms, the medical conditions
studied, the type of algorithm, the type of histological staining (image
type), the number of patients included in the study, the magnification levels
of the images that fed the models, the data augmentation techniques used, the
place of origin of the data (data), the geographical location of the
institution that developed the work and the bibliographic reference (Table
III).

TABLE III: Publication analysis of relevant works in Renal computational
pathology. GLR, glomeruli; TU, tubule; AR, artery; FB, fibrosis; RO, rotation;
Fl, flip; CON, contrast; CR, crop; COL, color; SCA, scaling; BR, brightness;
TRA, transpose; NOI, noise; ELA, elastic deformation; DS, digital staining as
augmentation; HUE, hue color space transformation

Use | Conditions | Algorithm | Image type | Patients | Magnification (x) | Augmentations | Data | Location | Year | Reference |
---|---|---|---|---|---|---|---|---|---|---|---
GLR detection | Transplant | YOLOv3 | HE | 10 |  | RO, FL, CON | Hannover Medical School | Germany, France | 2020 | [61] |
GLR detection | N/A | faster R-CNN | HE, PAS | N/A | 200, 400 | N/A | Taichung Veterans Hospital | Taiwan | 2018 | [64] |
Condition detection | Diabetic nephropathy | CNN | IF | 83 | N/A | N/A | Okayama University | Japan | 2020 | [90] |
GLR segmentation | Chronic kidney disease | Inception v3 CNN | Masson | 171 | N/A | NOI | Boston Medical Center | United States | 2019 | [65] |
Condition detection | Global, segmental sclerosis, | InceptionV3 | PAS, PAM | 283 | N/A | N/A | Kyoto University Hospital | Japan | 2020 | [89] |
GLR segmentation | Multiple kidney disease | Cascade Mask R-CNN | HE, PAS, PAM, Masson | 664 | N/A | RO, FL, CR, TRA | Peking University | China | 2021 | [66] |
GLR segmentation | Transplant | U-Net | PAS | 101 | 200 | RO, FL, ELA , SCA, CR, NOI, blur, CON, BR | Radboud Medical Center | Netherlands | 2019 | [67] |
GLR, TU, AR segmentation | Tubulointerstitial nephritis | U-Net | PAS | 21 | N/A | RO, FL, CON | Kanazawa Hospital | Japan | 2022 | [68] |
Stain generation | Non tumoral renal disease | GAN | HE, PAS, Masson, Jones | 58 | 200 | DS | UCLA | United States | 2021 | [91] |
Lesion detection | Ig A nephropathy | LSTM-GCNet, V-Net | PAS | 400 | 400 | N/A | Jinling Hospital | China | 2020 | [80] |
GLR, TU, AR, FB segmentation | Transplant | CNN, U-Net | PAS | 15 | 200 | RO, TRA, ELA | Radboud Medical Center, | Netherlands | 2018 | [69] |
GLR detecion | Glomerulosclerosis | CNN | HE | 17 | 200 | FL, RO, TRA | Washington University | United States | 2018 | [70] |
Cancer cell detection | Renal cell carcinoma | CNN | HE | 2093 | N/A | FL, CR | The Cancer Genome Atlas | Global | 2019 | [84] |
Cancer cell detection | Renal cell carcinoma | CNN | HE | 42 | 200 | N/A | The Cancer Genome Atlas | United States | 2020 | [85] |
GLR segmentation | N/A | U-Net | HE, PAS, Masson, Silver | 125 | 400 | N/A | Neptune repository | United States | 2021 | [71] |
GLR detection | N/A | CNN, HOG | HE, PAS, Jones | 26 | 400 | N/A | Hannover Medical School | Germany, France | 2017 | [72] |
FB detection | FB | glpathnet | Masson | 64 | 400 | N/A | Wexner Medical Center | United States | 2021 | [74] |
GLR detection and classification | Membranous nephropathy | YOLOv3, MIL | PAM | 1281 | 400 | N/A | Shanxi Medical University | China | 2020 | [73] |
Stain generation | Transplant | GAN | PAS, IF | 20 | N/A | N/A | N/A | N/A | 2021 | [92] |
Lesion detection | Transplant | CNN | HE, PAS, Jones | 2282 | N/A | N/A | Netherland hospitals | Netherlands | 2022 | [81] |
GLR detection and classification | Lupus nephritis | YOLOv4 | PAS | 163 | N/A | FL, RO, SCA, TRA, HUE, NOI | Xijing Hospital | China | 2021 | [74] |
GLR detection | Transplant | Faster R-CNN | N/A | 19 | N/A | RO | Bari University Hospital | Italy | 2020 | [75] |
GLR and TU segmentation | Glomerulosclerosis | U-Net | PAS | 83 | 100 | FL, RO, SCA | Citta della Salute | Italy | 2021 | [76] |
GLR detection | Glomerulosclerosis | VGG16 | HE | 84 | 200 | FL, RO | Washington University | United States | 2021 | [77] |
Macrophage, lymphocite detection | Transplant | U-Net | IHC | 22 | N/A | N/A | Hannover Medical School | Germany | 2021 | [93] |
Condition detection | amyloidosis, diabetic glomerulosclerosis | N/A | EM | 600 | N/A | N/A | Northwell Health | United States | 2021 | [88] |
Interstitium, TUs segmentation | Transplant | R-CNN, U-Net | PAS | 93 | 200 | N/A | N/A | United States | 2022 | [98] |
GLR segmentation | Normal tissue | Mask-RCNN | IF, PAS | N/A | N/A | N/A | N/A | United States | 2021 | [78] |
Cancer cell detection | Renal Cell Carcinoma | CNN | HE | 44 | 400 | RO, FL, SCA | Louisville Hospital | United States | 2022 | [86] |
Deposition detection | Membranous nephropathy | CNN | Hyperspectral | 20 | N/A | N/A | China Friendship Hospital | China | 2021 | [57] |
Peritubular capillaries detection | Transplant | CNN | IHC | 380 | 200 | RO, FL, SCA | Asan Medical Center | South Korea | 2019 | [94] |
GLR detection | N/A | AlexNet | HE, PAS, Silver, Masson | 28 | 400 | N/A | Dartmouth Medical Center | United States | 2020 | [79] |
Vascularity detection | Renal cell carcinoma | SVM | HE, IHC | 8 | 400 | N/A | Cedars Medical Center | United States | 2017 | [95] |
Cancer cell detection | Renal cell carcinoma | ResNet | HE | 486 | 200 | FL, RO, COL | Dartmouth Medical Center | United States | 2021 | [87] |
Glomerular deposits | N/A | ResNet | IF | 2542 | 400 | FL, RO | University of Modena, | Italy | 2020 | [96] |
Lesion detection | IgA, membranous, diabetic nephropathy | AlexNet | IF | 1608 | N/A | N/A | People’s Liberation Hospital | China | 2021 | [82] |
Podocyte segmentation | Antibody–associated glomerulonephritis | U-Net | IF | 317 | N/A | N/A | Eschweiler Medical Center | Germany | 2021 | [97] |
Lesion detection | Podocytopathy | VGG19 | HE, PAS, PAM | 835 |  | FL, RO, BR, SCA | Goncalo Moniz Institute | Brasil | 2022 | [83] |