An Open-Source Approach for Digital Prostate Cancer Histopathology:

Bringing AI into Practice

Markus Bauer

1,∗

, Lennart Schneider

4,∗

, Marit Bernhardt

, Christoph Augenstein

, Glen Kristiansen

and Bogdan Franczyk

2,3

ScaDS.AI, University of Leipzig, Germany

University of Leipzig, Germany

Wroclaw University of Economics, Poland

Institute of Pathology, University Hospital Bonn, Germany

Keywords:

Artiﬁcial Intelligence, Vision Transformer, Self-Supervised Learning, Digital Pathology, Prostate Carcinoma.

Abstract:

The histopathological analysis of prostate tissue is challenging due to the required expertise and the inherently

high number of samples. This accounts especially for prostate cancer (PCa) assessment (tumour grading), as

parameters like the Gleason score have high prognostic relevance, but suffer from signiﬁcant interobserver

variability, mainly due to individual grading practice and experience. AI-based solutions could assist patho-

logical workﬂows, but their integration into clinical practice is still hampered, as they’re optimised based on

general AI-metrics, rather than clinical relevance and applicability. Moreover, commercial solutions often

provide similar performance than academic approaches, are expensive, and lack ﬂexibility to adapt to new use

cases. We investigate the requirements to provide a ﬂexible AI-based histopathological tissue analysis tool,

that makes the expertise of experienced pathologists accessible to every hospital in a user-friendly, open-source

solution. The proposed software allows for slide inspection, tumour localisation and tissue metric extraction,

while adapting to different use cases using a Python-enabled architecture. We demonstrate the value of our tool

in an in-depth evaluation of transurethral hyperplastic resection tissue (TURP)-chip analysis and PCa grading

using a set of extensively annotated prostate cancer patient cases. Our solution can support pathologists in

challenging cases, fasten routine tasks and creates space for detail analysis.

1 INTRODUCTION

Developing tools for digital pathology of the Prostate

Carcinoma (PCa) involves addressing challenges re-

lated to handling massive gigapixel whole slide im-

ages (WSIs) and ensuring high security and privacy

standards. These tools have signiﬁcant potential to

enhance pathologists’ work, including the integration

of AI for various attractive use cases. AI-based grad-

ing, exempliﬁed in digital pathology, stands out as

a prominent application. Recent FDA-cleared prod-

ucts and ongoing research highlight the efﬁcacy of

AI methods in grading Prostate Carcinoma (PCa).

Notably, studies demonstrate high accuracy in repli-

cating the histopathological Gleason grading system,

achieving a quadratically weighted κ of over 0.85

(Perincheri et al., 2021; Tolkach et al., 2020; Bul-

ten et al., 2022). The availability of extensive pub-

These authors contributed equally to this work.

lic cohorts for PCa WSIs and core needle biopsies

(CNBs) has spurred the development of explainable

algorithms for PCa grading (Zuley et al., 2016; Bul-

ten et al., 2022). Moreover, the emergence of self-

supervised learning (SSL) methods like MoCov2, Di-

nov2, and I-JEPA (Chen et al., 2020; Oquab et al.,

2023; Assran et al., 2023) has facilitated training on

large datasets without the need for time-consuming

labeling procedures. Leveraging architectures like vi-

sion transformers that excel in capturing local-global

feature relationships in images, these approaches re-

duce the effort required for model creation while

achieving state-of-the-art results (Chen and Krishnan,

2021; Lu et al., 2021; Yang et al., 2023). Despite their

effectiveness, such systems are infrequently observed

in pathological practice, primarily due to two factors:

First, current AI solutions lack depth in address-

ing speciﬁc problems and often focus on evaluations

by a limited number of medical experts (Eloy et al.,

2023), typically using either small cohorts or address-

Bauer, M., Schneider, L., Bernhardt, M., Augenstein, C., Kristiansen, G. and Franczyk, B.

An Open-Source Approach for Digital Prostate Cancer Histopathology: Bringing AI into Practice.

DOI: 10.5220/0012681900003690

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 1, pages 729-738

ISBN: 978-989-758-692-7; ISSN: 2184-4992

729

ing superﬁcial problems (e.g., benign vs. “possible”

tumour) (Perincheri et al., 2021). Such approaches

are not easily comparable on a broader scale and

are typically expensive, particularly with commer-

cial providers. State-of-the-art solutions also exhibit

weaknesses in handling ISUP grade groups (c.f. sec-

tion 2.1) 2 and 3 (Bulten et al., 2022). Especially

those grade groups, however, most urgently require

reproducible, computer-aided assistance as they are

known for high inter- and intra-observer variability.

Second, to establish a digital pathology tool, it is

crucial to keep the efforts of integrating it low. Yet,

there is currently no easy-to-integrate open-source

software that enables hospitals to experiment with AI

solutions. Especially, published approaches require

technical expertise that hospitals in rural areas can’t

provide. Factors such as varying digitalisation levels

and preparation practices add to the technical burden

of AI adoption (Kartasalo et al., 2021). For example,

staining differences among hospitals can signiﬁcantly

impact performance (Singhal et al., 2022), necessi-

tating advanced concepts like self-supervised learn-

ing (Ye and Wang, 2022) or generative models (Park

et al., 2020), which may be impractical for hospitals

lacking machine learning experts. Additionally, exist-

ing inspection tools have limited capabilities of cus-

tomisation and thus lack ﬂexibility to integrate new

use cases, e.g., image stitching or measuring metrics

like the tissue area size.

Hence, in this paper we design a tool that enables

the integration of computer-aided histopathology in

clinical workﬂows, regardless of the actual use case.

The contribution of our paper is as follows:

• We introduce and discuss the practical require-

ments of integrating automated tissue analysis

into clinical practice. Two use cases are provided

to underline the necessity of these requirements.

• We provide an in-depth investigation of AI’s cur-

rent capabilities in PCa localisation and grading

using a set of patient cases with detailed annota-

tions.

• We present a novel approach to reading and visu-

alising gigapixel images resource-efﬁciently.

2 METHODS

To implement a concise solution for computer-aided

histopathology, we deﬁne multiple functional (F) and

non-functional (NF) requirements. We consider a

broad variety of expert perspectives by incorporat-

ing the thoughts of pathologists of different experi-

ence levels, AI researchers as well as medical data

and database experts. Additionally, we consider the

requirements as presented in (Hor

ak et al., 2023). For

our solution, the requirements, and the reason for in-

cluding them are shown in Tab. 1.

2.1 Data and Code Used in this Study

For our evaluation, we collected nine patient WSIs

from a currently collected, private dataset of radi-

cal prostatectomies (RPEs) that have been extensively

annotated. In total, 3875 tumour regions have been

annotated and analysed to emphasise the capabilities

and limitations of AI in detail, rather than only con-

sidering the patient-level result. All cases contained

(as far as being found in the tissue) segmentations for:

Benign Gland, Hyperplasia (subtypes: Benign, Clear

Cell), Gleason Grade 3, 4 (subtypes: cribriform, con-

solidated, malformed, glomeruloid) and 5, Intraep-

ithelial Neoplasia (PIN) (subtypes: low grade, high

grade), Perineural Carcinosis, Seminal Vesicle, Intra-

ductal carcinoma (IDC-P), Immune cells, and, Atro-

phy. In addition to the RPE-WSIs, one real and nine

synthetic TURP-chip examples were included to eval-

uate tissue area measurement (c.f. section 2.3).

To access the malignancy of the WSIs used in this

study, the Gleason grading, an architecture-based sys-

tem, focusing exclusively on shape of tumour glands/

epithelia, was used, together with further annotations

for subtypes of known clinical relevance. Gleason

grading assigns numbers from 1 to 5 to each archi-

tectural pattern, with 5 indicating the most aggres-

sive cancers. In prostate cancer, being morphologi-

cally heterogeneous, the grading combines the most

common and second most common patterns to yield

the Gleason score (e.g., GS 3+4=7). Adjustments

to Gleason grading, originally introduced in 1966,

consider empiric data on prognostically important

factors, leading to speciﬁc grading rules for radical

prostatectomies and biopsies and other changes (Ep-

stein et al., 2016). In the revision from 2014, the Inter-

national Society of Urological Pathology (ISUP) has

additionally introduced ﬁve prognostically different

Grade Groups to categorise Gleason scores (Epstein

et al., 2016).

2.2 AI-Based Tumour Localisation and

Grading

The ﬁrst use case to evaluate our proposed solution is

the determination of malignant tissue in RPEs. Re-

lated work typically focuses on detecting or localis-

ing (

= detection with position estimate) tumours in

CNBs (Bulten et al., 2022; Perincheri et al., 2021),

as here AI can already lead to signiﬁcant time-to-

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Table 1: List of requirements that were analysed for this publication.

Requirement Reason

NF #1 Python Compatibility Python must be supported ﬂexibly to make the software suited for di-

verse computer vision use cases.

NF #2 PyTorch Compatibility Pretrained models should be natively loadable to decrease implemen-

tation efforts and ensure ﬂexibility.

NF #3 Visualisation Capabilities Analyses need to be presented in a meaningful overlay or text window,

to avoid information loss.

F #4 Efﬁciency & Scalability Few computation and storage resources should be used, to allow mid-

tier work station compatibility.

F #5 Maintainability A wide-spread language with, e.g., natively thread-safe operations

should be used to guarantee long-term support and stability of the so-

lution.

F #6 Privacy, Security Data privacy needs to be considered as well as vulnerability of the

technologies used, as hospitals are part of the critical infrastructure.

F #7 Open-Source A desktop-application can be created to impose low technical burden

and thus attract a wide audience.

F #8 Platform Independent The software needs to be available on at least Windows and Linux, to

provide a base for both medical and AI experts.

diagnosis reduction (da Silva et al., 2021). For RPEs,

however, applying AI on one hand has an even higher

value but also requires more practical considerations

due to the large amount of tissue compared to the

CNBs. Hence, we focus on evaluating RPEs.

As digital PCa pathology is a vital research ﬁeld,

various solutions have been proposed. Earlier works

focus on tissue microarrays and use per-image or

at least on region-level annotations and process the

images using convolutional neural networks (CNNs)

(Arvaniti et al., 2018; Nagpal et al., 2019). These

approaches have the inherent downside of requiring

detailed annotations, which limits the range of avail-

able data signiﬁcantly. More recent solutions stick

with CNNs like Resnets or Efﬁcientnets (Bulten et al.,

2022), but include a solution to train using the patient-

level label.

This can be done by adding an attention mech-

anism rather than a simple multilayer perceptron as

the model head that efﬁciently connects the features

Z of multiple smaller parts (patches) of the same WSI

to predict one label. Attention mechanisms were

proposed as part of Transformers (Vaswani et al.,

2017), and compute softmax-normalised attention

scores through the dot product between query (Q)

and key tensors (K). Mathematically, this can be ex-

pressed as

Attention(Q, K,V ) = softmax



√



·Z (1)

with, d

as the key tensor’s dimensionality. This al-

lows the model to dynamically assign importance to

different parts of the input sequence, enhancing its

ability to capture intricate relationships during pro-

cessing. While this was found to be an excellent op-

tion in AI-based grading, two issues remain with the

approaches as, e.g., presented by multiple teams in

(Bulten et al., 2022). First, their scenario is still super-

vised and thus limited to labelled cases, which signif-

icantly reduces the amount of available training data.

Furthermore, the use of CNNs results in the need for

visualisation methods such as Grad-CAM (Gildenblat

and contributors, 2021) for locating the actual tumour,

which decreases the inference speed.

A combined solution for addressing both of these

problems lies in SSL training of a vision transformer

(ViT) (Dosovitskiy et al., 2020). Among many

methods recently proposed, contrastive unsupervised

learning is a robust and widespread paradigm de-

signed for training models without explicit labels.

The method revolves around mapping similar in-

stances closer together while pushing dissimilar in-

stances apart, and aims to learn high-dimensional rep-

resentations that effectively capture the underlying

structure of the input data. The loss function em-

ployed is often the InfoNCE (Noise-Contrastive Es-

timation) loss, given by

L = −log

exp(sim(z

, z

))

exp(sim(z

, z

)) +

∑

j=1

exp(sim(z

, z

−

))

(2)

where sim(·, ·) denotes the similarity function, z

, z

and z

−

represent the positive, anchor and negative

samples, and N represents the total number of neg-

ative samples. Positive samples are generated by im-

age augmentation of the anchor, while negative ones

are deﬁned as all the other images in a batch.

An Open-Source Approach for Digital Prostate Cancer Histopathology: Bringing AI into Practice

731

2.3 TURP-Chip Measuring

Benign Prostatic Hyperplasia (BPH) commonly af-

fects men aged 40 and above, causing obstructive uri-

nary symptoms due to hyperplasia in the prostate’s

transitional zone. The exact cause involves a com-

plex interplay of hormonal, genetic, and environmen-

tal factors, with hormonal changes (e.g., increased es-

trogen, decreased testosterone) assumed to play a ma-

jor role. Clinically, BPH presents as Lower Urinary

Tract Symptoms (LUTS), including hesitancy, incom-

plete emptying, and nocturia.

The primary surgical approach for Benign Pro-

static Hyperplasia (BPH) is transurethral resection of

hyperplastic tissue. Various techniques are used for

tissue retrieval, followed by histological analysis to

conﬁrm the BPH diagnosis and rule out malignancy.

Although prostate cancer (PCa) is relatively rare in

BPH cases, it occurs in approximately 5% of unse-

lected cases due to the typical age of patients that cor-

relates with cancer prevalence.

The pathology laboratory’s analysis of TURP

chips involves ﬁxation, embedding, and sectioning

before microscopic examination. The debate over the

extent of tissue embedding to safely exclude an unsus-

pected prostatic carcinoma has persisted. Approaches

vary from full embedding to selective embedding,

such as using 10 histology cassettes (K

ollermann

et al., 2022). Incidental prostate cancer (iPCa) detec-

tion rates are consistently based on weight analyses

(WA). While WA is an accepted parameter, the size or

number of individual TURP-chips has received lim-

ited attention. Given the signiﬁcant variation in chip

sizes among surgeons and their preferred methods, we

reason that procedures generating smaller chips may

systematically neglect coexistent carcinoma.

To explore the correlation between iPCa detection

rates and chip sizes, and to assess the distribution of

chip sizes based on surgical techniques, an automated

tissue measurement solution is needed. Hence, we

use this scenario as the second use case and an appli-

cation for ﬂexibly adapting our proposed solution to

new tasks. While clinically highly interesting, from

a computer vision perspective, this task can be auto-

mated with established methods, by implementing an

area measurement component in Python. The WSI

image is loaded, and the pixel resolution is accessed,

which is supplied as an object property in the Python

OpenSlide object. With the image downscaled by a

factor of 64, a pipeline of operations is performed.

First, tissue is segmented using Otsu-

thresholding which creates a binary image (bin).

Smaller/unconnected tissue areas are excluded using

a median ﬁlter. Additionally, a cascade of 10 erosion

steps (kernel size 1) is performed on the ﬁltered

image to ensure each individual chip will yield at

least one larger connected region. In the eroded

image, each connected region is then assigned a

unique value. Afterwards, individual regions greater

than 50×50 µm are extracted and enlarged again using

a cascade of 10 dilations (kernel size 1). Finally,

region area size is determined and saved together

with the total area size. The procedure is described in

the pseudocode Alg. 1.

WSI ←X

i, j

;

mppx ←slide.mppx; // slide=OpenSlide obj

mppy ← slide.mppy ; // mpp=µm per pixel

tresh ←otsu(WSI);

bin ← WSI

x,y

: (x, y) ∈ {(x

′

, y

′

) | X[x

′

, y

′

] > 0};

bin ←median(bin, disk(5));

total ← 0;

for i ← 0 to 9 do

bin ← erosion(bin)

end

lbin ←label(bin);

N ← unique(lbinary);

for n ← 0 to N do

obj ← lbin

x,y

: (x, y) ∈ {(x

′

, y

′

) | X[x

′

, y

′

] = n};

size ← |

ob j

if size ·mppx ·mppy ≥ 50 ·50 then

part ← clip(obj, 0, 1);

for i ← 0 to 9 do

erosion(bin) part ← dilation(part)

end

partsize ← |part|;

save(partsize);

total ←total + partsize;

end

save(total)

Algorithm 1: Algorithm used to count tissue in TURP-

chips.

2.4 Slide Viewer

Loading and inspecting WSIs is a challenging task,

as this requires operating in Gigapixel scales. A

typical image has a size of ≈ 120000 ×80000 pix-

els and requires 2 GB of memory, whereas images

can even be signiﬁcantly larger. Thus, loading the

image into memory is no viable option. Further-

more, typical pathology workstations don’t provide

top-tier computation resources. Hence, a wide variety

of solutions to visualise and edit WSIs is available.

The type of technology used is extremely heteroge-

nous, ranging from web-based approaches (CodePlex

Foundation and OpenSeadragon contributors, 2009;

Hor

ak et al., 2023; Sch

ufﬂer et al., 2022) to Java- or

C++ applications with some exported Python func-

tions (Bankhead et al., 2017; Radboud Computational

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

732

Pathology Group and ASAP contributors, 2009), or

even more experimental setups that leverage gaming

consoles (Yagi et al., 2012) for image processing. Yet,

none of these approaches seemed to cover our require-

ments as most of them require at least a large amount

of RAM and drive storage, and none of these solutions

offers a sufﬁcient compatibility for Python scripts and

PyTorch models. Especially, the web-based applica-

tions would violate requirements #6 and #7 (c.f. Tab.

1), as the underlying JavaScript is not only known for

exposing vulnerabilities that could be a potential risk

for clinics but also doesn’t provide, e.g., typing capa-

bilities which can be disadvantageous for the actual

software development. For (Bankhead et al., 2017)

at least a workaround to incorporate PyTorch models

is available, but it requires manual usage of Groovy

scripts which is inconvenient to use for pathologists,

as well as prone to usage errors, as the scripts can’t be

embedded to the UI but rather use a developer con-

sole.

To implement a viewer supporting the execu-

tion of Python scripts in the Rust programming lan-

guage (Matsakis and Klock II, 2014), we designed a

resource-efﬁcient image loading algorithm, outlined

in a simpliﬁed form in Algorithm 2. The code for

this study will be available on GitHub, offering more

detailed insights into the actual implementation. In

the run() function of Algorithm 2, two arrays, cache

and preload cache, are allocated with an example

size of 2048×2048 (twice the viewport). Start po-

sitions and offsets (0, 0) are set, along with an up-

date available variable signalling the completion of

preloading, initialised to false. Note that the latter

is marked with σ when used, indicating its thread-

safe nature to prevent data races. Before the cycle,

an initial image part is loaded into the RAM using

precache update. This routine reads new data to the

preload cache based on the whole slide image and

current position. The capture() functions actively

avoid race conditions. Rust supports similar function-

ality to this pseudocode. Delta positions, coords, are

collected by tracking the user’s dragging of the im-

age. Using a cache offset variable to accumulate these

deltas, parts of the image at the currently dragged po-

sition are read from the RAM and visualised. This op-

eration is fast enough to work without latency. The vi-

sualisation component processes only a viewport part

of the actual cache (arrays allocated in the RAM),

utilising a reference to the cache array. Once the user

drags the image above half the viewport size, new data

is read into the preload cache array using a separate

thread. The function receives a thread-safe reference

to the preload cache array, allowing the user to con-

tinue dragging the image while updates are rendered.

Position shifts are recorded in an additional variable,

preload offset, from this point to correct the start po-

sition once the cache is updated. When the cache limit

is reached, offsets are set/reset, and the preloaded data

is copied to the cache array within the RAM, ensuring

a seamless update without noticeable delays.

Function precache update(ptr, threadskip, pos):

slide; // The OpenSlide image

threadskip.capture();

if ∼threadskip then

update data(slide, ptr, pos);

threadskip ← true;

threadskip.release();

end

Function run():

init(viewportsize, cache, preload cache,

update available, loading started, offset,

cache offset, preload offset, coords);

user inputs ← Φ

x,y

|(x, y) ∈ viewportsize;

precache update(σ(*preload cache),

update available, offset);

sync(*cache, cache offset, σ(*preload cache));

for i ←1 to inf do

delta ← Φ

;

coords ← coords + delta;

cache offset ← cache offset + delta;

if exceeds(viewportsize / 2, cache offset) &

!loading started then

offset ← coords;

thread(precache update(σ(*preload cache),

update available, offset));

loading started ← true;

end

if loading started then

preload offset ← preload offset + delta;

end

if exceeds(viewportsize, cache offset) then

update ready.capture();

cache offset ← preload offset;

preload offset ← 0;

loading started ← false;

sync(*cache, σ(*preload cache))

end

vis(*cache, cache offset, viewportsize)

end

Algorithm 2: A novel algorithm for efﬁcient image region

loading was implemented.

3 RESULTS

The proposed algorithms have been integrated as a

Rust (version 1.75.0) application, as Rust offers great

support for multithreading, type safety, and cross

compilation of executables (Linux and Windows c.f.

Tab. 1 #7&8). For script-support Python3.11 was

used. All PyTorch models used were trained in a grid

search for optimal parameters and cross validated us-

An Open-Source Approach for Digital Prostate Cancer Histopathology: Bringing AI into Practice

733

viewport, cache

Slide Rendering

Python Bridge

Data Loading

Torch Model Lib

Main Application

TURP Measurement

Info View

PCa Heatmap

Python Modules

viewport

overlay

viewport

infos

data

Figure 1: Architectural overview of the tool.

ing data taken from (Zuley et al., 2016) and (Bulten

et al., 2022) before considering them in this work.

Performance measurements of the application were

purposely collected using a regular workstation (In-

tel(R) Core(TM) i7-9750H CPU @ 2.60GHz, 16GB

RAM, 512GB SSD), to be comparable to the actual

application in clinical practice. No GPU was used

during inference time.

The implemented architecture is relatively

lightweight, which on one hand is a result of the

iterative character of this project but also an approach

to comfort requirement #5 by keeping the complexity

as low as possible. Fig. 1 shows a principle schematic

of the software.

The components can be divided into a main ap-

plication and Python modules, whereas both can also

be run independently, which allows a consistent script

behaviour during development and program execu-

tion. Two scripts have been added for this work,

which can predict a heatmap of cancer probability for

a whole slide image or measure the area of the dis-

played tissue. New tools can simply be added as re-

quired.

The main application consists of ﬁve components.

The data loading module performs preloading, po-

sition acquisition and cache synchronisation as de-

scribed in Alg. 2. It ensures a latency-free data load-

ing procedure with low memory and disk footprint

(c.f. Tab. 1 #4). The Torch model lib can directly

load a TorchScript-saved AI model and run an infer-

ence cycle on the current viewport (c.f. Tab.1 #2).

Analogously, the Python bridge is the interface for

running Python scripts and transporting data between

it and the application (c.f. Tab.1 #1). It can be used to

run any Python script and return either a dictionary

(”infos”) of infos or a heatmap image (”overlay”).

The slide rendering and info view module visualise

the data, which can be raw or AI-prediction-overlaid

images, as well as text information. The displayed in-

formation can be controlled from within the Python

scripts and thus be adapted for particular use cases

(c.f. Tab. 1 #3).

When testing the viewer, we could not recognise

signiﬁcant delays when dragging the image. The tests

were run using a 201640×87725 WSI. The only delay

of less than a second happens when zooming in. Fur-

thermore, the memory usage was very low, with only

around 100MB RAM. Tab. 2 provides an overview of

performance metrics of our solution and other view-

ers.

We ﬁnd our solution to be comparably fast as the

others, while sharing the lowest RAM usage with

xOpat (Hor

ak et al., 2023). Only a slight delay can be

recognised when zooming in, which we ﬁnd accept-

able as a tradeoff for the low resource requirements.

xOpat (Hor

ak et al., 2023) runs comparably efﬁcient

but requires beforehand conversion of the data to a

web-compatible format (such as TIFF), resulting in

an unacceptable loading time. ASAP (Radboud Com-

putational Pathology Group and ASAP contributors,

2009) has the lowest CPU utilisation, but, similar to

our solution, shows a light delay when zooming (bet-

ter said when dragging the zoomed version) and has

the highest RAM usage together with QuPath. We

found it difﬁcult to determine the exact RAM amount

required for ASAP, as RAM usage increases while

dragging the image. QuPath has the overall worst

values. From our analysis, we conclude that our so-

lution offers a good mix of the compared solutions’

strengths, while also being more ﬂexible by support-

ing Python.

3.1 TURP-Chip Measuring

As a ﬁrst evaluation task, TURP-chips were pro-

cessed to measure their individual area using the al-

gorithm described in section 2.3. As a ﬁrst trial,

we created nine artiﬁcial images with patches ran-

domly cropped from an actual RPE image. Evalua-

tion showed that for patches containing Hyperplasia,

multiple instances may be detected, as some tissue ap-

pears disconnected. While this behaviour appears un-

expected for RPE-WSIs, it is desired for TURP-chip-

WSIs as here even smaller chips with few distance to a

larger tissue area should be detected separately. That

way, a threshold of a minimum chip size to be consid-

Table 2: Comparison of required computation resources

among different slide viewers.

Viewer CPU RAM t

init

zoom

QuPath ∼120% ∼5GB ∼3s <50ms

ASAP ∼50% >4GB ∼1s <1s

xOpat ∼75% ∼300MB ∼180s <50ms

Ours ∼120% ∼300MB ∼1s ∼1s

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

734

ered can be set in later analysis. To ﬁlter parts that are

likely no tissue but rather preparation artefacts, we,

however, added an initial threshold of 2500 (µm)

The results could be conﬁrmed in a real-tissue test

cases. Fig. 2 shows the UI after the real-tissue test

case has been analysed.

The WSI or a folder of WSIs can be loaded in the

menu, as well as the processing script. The script will

analyse each WSI upon clicking “Analyse” and write

the tissue area of each detected chip to a CSV ﬁle. The

medical experts will receive an immediate summary

of the number of chips detected together with the total

area to allow easy access to the extracted information.

3.2 Tumour Localisation and Grading

To assist the localisation and grading procedure,

Resnets and Efﬁcientnets as suggested by (Bulten

et al., 2022) and a CTranspath-based model as sug-

gested by (Yang et al., 2023) were integrated into our

tool. The results obtained with ResNet and Efﬁcient-

Net models, as recommended by (Bulten et al., 2022),

could not be replicated using our internal dataset,

which is likely caused by variation in staining pro-

tocols across hospitals. To mitigate this issue, we em-

ployed a GAN model, following the approach sug-

gested by (Park et al., 2020), and trained it on the

Radboud portion of the PANDA dataset (Bulten et al.,

2022) using default parameters (c.f., github.com/

taesungp/contrastive-unpaired-translation) for 100

epochs. Upon normalising our images using the

GAN, the patient-level κ score only slightly de-

creased compared to the PANDA dataset (Bulten

et al., 2022), registering at 0.75. However, the GAN-

based preprocessing signiﬁcantly increased the pro-

cessing time from approximately 3.1 to 10.3 seconds

per 1120×1120 image patch. In practical terms, this

translates to a processing time of 5 minutes with-

out normalisation and 15 minutes with normalisation

for the test image discussed in Section 3.1. Further-

more, to localise the tumour, the Grad-CAM method

(Gildenblat and contributors, 2021) needed to be

Figure 2: The proposed tool enables pathologists to analyse

TURP-chips and especially to measure their tissue area.

Table 3: Average region-level f

-scores of the predic-

tions per patch, calculated using the attention matrix of the

model, and the annotated ground truth on the test cohort for

the main GG.

as Benign Tumour

Benign 0.80 ± 0.16 0.20 ± 0.16

Tumour 0.21 ± 0.16 0.79 ± 0.16

GG 3 0.16 ± 0.14 0.84 ± 0.14

GG 4 (crib.) 0.16 ± 0.19 0.84 ± 0.19

GG 4 (glom.) 0.40 ± 0.37 0.60 ± 0.37

GG 4 (malf.) 0.17 ± 0.11 0.83 ± 0.11

GG 4 (cons.) 0.27 ± 0.23 0.73 ± 0.23

GG 5 0.53 ± 0.16 0.47 ± 0.16

used, which not only contributes to the high process-

ing time, but also yields only visualisations that lack

a sufﬁcient overlap with the tumour and in particular

appear counterintuitive (e.g., stroma is marked, but

glands are omitted).

Hence, these models were discarded for the

CTranspath-based model (Yang et al., 2023), which

uses an SSL-pretrained backbone that incorporates a

considerable collection of different tumour’s WSIs.

Promising region-level results could be achieved,

which can be found in Tab. 3 and 4. Besides the

Gleason grades (GGs), perineural carcinosis and IDC-

P refer to the tumour class. The overall region-level

-score was 0.80 ±0.09, whereas the highest tumour

true positive was achieved for GG 3 and the lowest

one for GG 5. For benign tissue, a true positive of

0.80 ±0.16 was achieved.

For GG 5, the low f

-score may be related to the

fact that such tumours are rare and thus only a few

examples exist in current datasets. Another potential

cause could be confusion to stromatic tissue, which

hasn’t explicitly been trained as a class.

GG 4 subpatterns (cribriform, malformed, con-

solidated) are identiﬁable, except for the glomeruloid

type, which is often confused with benign tissue. Fur-

ther investigation revealed that glomeruloid patterns

were correctly classiﬁed within more complex GG4

Table 4: He result of Tab. 3 remains consistent for tissue

subtypes, although some subtypes show a remaining weak-

ness of the model.

as Benign Tumour

Benign Hyperpl. 0.74 ± 0.32 0.26 ± 0.32

High-Grade PIN 0.57 ± 0.38 0.43 ± 0.38

Low-Grade PIN 0.92 ± 0.00 0.08 ± 0.00

Perineural Carc. 0.30 ± 0.19 0.70 ± 0.19

Seminal Vesicle 0.85 ± 0.15 0.15 ± 0.15

Clear Cell Hyperpl. 0.80 ± 0.00 0.20 ± 0.00

IDC-P 0.30 ± 0.18 0.70 ± 0.18

Atrophy 0.25 ± 0.34 0.75 ± 0.34

An Open-Source Approach for Digital Prostate Cancer Histopathology: Bringing AI into Practice

735

Figure 3: Predicted tumour (left; orange ISUP group 2, red ISUP group 3) and ground truth (orange Gleason Grade 3, red

Gleason Grade 4). Especially for cases with a high tumour amount, the proposed solution achieves a high overlap ( f

-score

0.90) of annotated and predicted tumour.

regions. Achieving precise localisation of individ-

ual glomeruloid glands may require an advanced sam-

pling strategy during training, leveraging a few WSIs

with extensive region annotation, as proposed in this

work. Despite these efforts, the current performance

is deemed insufﬁcient, and addressing this issue will

be a focus of future work.

We further investigated the classiﬁcation rates of

different benign and tumour tissue subtypes (c.f. Tab.

4). Generally, plausible results could be achieved.

Especially, the correct classiﬁcation of hyperplasia

yields great value, as it could also be used in TURP-

chip analysis. For High-Grade-PIN, the classiﬁcation

as tumour is signiﬁcantly higher than for Low-Grade-

Pin. This seems plausible, as High-Grade-PIN is con-

sidered a precursor of prostatic carcinoma. Partic-

ularly Low-Grade-PIN also suffers from high inter-

oberserver reproducibility. Thus, the AI predictions

meet the expectation, as Low-Grade-PIN not neces-

sarily refers to tumour tissue (Epstein et al., 1995).

Furthermore, perineural carcinois and IDC-P have a

good score, which is already reﬂected in the overall

-score. They are anyway listed here for better read-

ing. The only signiﬁcant misclassiﬁcation observed

was for atrophies.

As the results for tumour subtype analysis looked

promising, we used the attention matrix of our model

to visualise the predicted tumour probability, whereas

deeper red signals a higher GG. Fig. 3 shows the re-

sults for the highest f

-score’s WSI. Generally, the

tumour region is matched well and even a grading of

different tumour areas of annotated GG 4 (red) and

GG 3 (orange) can be distinguished. Even for the

case of the lowest f

-score (c.f. 4), this can be re-

produced in particular. Overall, the model’s strength

as of now, however, clearly needs to be considered in

tumour localisation rather than grading. For exam-

ple, in Fig. 3 and 4 the annotations suggest GG 3

(likely ISUP 1) and GG 4 (likely ISUP 4), while the

algorithm mostly predicts ISUP 2 and ISUP 3. This

is plausible, as a possible combination for tumour pat-

terns in the individual patches could indeed be ISUP 2

(=95% of GG 3 and 5% of GG 4) and ISUP 3 (95% of

GG 4 and 5% of GG 3). Yet, there remains an uncer-

tainty about the exact GS the prediction implies. The

heatmaps created in Fig. 3 and 4 can be overlaid in

the viewer of the proposed solution and thus directly

support in real-world use-cases. Furthermore, a per-

patch overlay prediction allows for quick and iterative

exploration of the image.

Finally, we evaluated the performance of different

inference normalisation strategies to see if the above

results could be improved, similar as in the case of

the Resnet/EfﬁcientNet-based models. We used the

image from section 3.1 for evaluation. Our results in-

dicate that both modiﬁcations, namely using a GAN

for stain-adaption (Park et al., 2020) and statistics-

fusion normalisation (BN) with β = 0.85 (Chattopad-

hyay et al., 2022), didn’t add value. For the GAN

-score improved by 0.005 and for the BN it even

decreased by 0.004. The SSL learning strategy and

the fact that the large data variability of the TCGA

(Zuley et al., 2016) (that the model was trained on)

appear to already prepare the model for transferring

it to different data. Considering the high processing

time of the GAN-based approach (1300s vs. 279s for

the base and 280s for BN model), we stick to the non-

normalised solution in favour of the tool’s usability.

4 CONCLUSIONS

In this paper, we presented a novel slide viewer that

efﬁciently loads and displays WSI images. The pro-

posed solution is ﬂexible and allows for use-case-

speciﬁc data processing and visualisation that can add

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

736

Figure 4: Prediction vs. ground truth (c.f. Fig 3). Slides with a lower tumour amount and high malignancy can generally be

graded, but result also in a lower f

score of 0.74.

great value to the pathological workﬂow. We demon-

strated the value of our work using two highly rel-

evant use cases. We conclude, that AI-based image

analysis as well as classical computer vision can as-

sist pathologists in their daily business. This can for

example be the case when analysing TURP-chips for

hyperplasia and iPCa or for tumour localisation. The

presented approach has the potential to reduce the re-

quired time-to-grade signiﬁcantly. We also uncovered

current systematic weaknesses of current state-of-the-

art AI solutions.

Future work should focus on closing these gaps,

e.g., by using sophisticated sampling strategies and by

focusing on currently underrepresented tumours pat-

terns such as GG 5. Furthermore, the image viewer

should be extended with more requirements and func-

tionality, such as training and annotation capabilities.

5 DATA AVAILABILITY

The training data used in this work is publicly avail-

able and can be found in (Bulten et al., 2022) and (Zu-

ley et al., 2016). The code and tool described and

used in this work can be found at https://github.com/

mbu93/OpenProsIT. The internal data set cannot be

shared due to privacy requirements.

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