thanminh01/thesis-data · Data Studio · Datasets at Hugging Face

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BRACS: A Dataset for BReAst Carcinoma Subtyping
in H&E Histology Images
Nadia Brancati1* , Anna Maria Anniciello2 , Pushpak Pati3,5 , Daniel Riccio1,4 , Giosuè
Scognamiglio2 , Guillaume Jaume3,6 , Giuseppe De Pietro1 , Maurizio Di Bonito2 , Antonio
Foncubierta3 , Gerardo Botti2 , Maria Gabrani3 , Florinda Feroce2 , and Maria Frucci1
1 Institute for High Performance Computing and Networking of the Research Council of Italy, ICAR-CNR, Naples,

Italy




arXiv:2111.04740v1 [q-bio.QM] 8 Nov 2021
2 National Cancer Institute – IRCCS – Fondazione Pascale, Naples, Italy
3 IBM Research – Zurich, Switzerland
4 University of Naples Federico II, Naples, Italy
5 ETH Zurich, Switzerland
6 EPFL Lausanne, Switzerland
* Corresponding author: nadia.brancati@cnr.it



ABSTRACT

Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women with cancer.
Recent advancements in diagnostic activities combined with large-scale screening policies have significantly lowered the
mortality rates for breast cancer patients. However, the manual inspection of tissue slides by pathologists is cumbersome,
time-consuming, and is subject to significant inter- and intra-observer variability. Recently, the advent of whole-slide scanning
systems have empowered the rapid digitization of pathology slides, and enabled to develop digital workflows. These progress
further enable to leverage Artificial Intelligence (AI) to assist, automate, and augment pathological diagnosis. But AI techniques,
especially Deep Learning (DL), require a large amount of high-quality annotated data to learn from. Constructing such
task-specific datasets poses several challenges, such as, data-acquisition level constrains, time-consuming and expensive
annotations, and anonymization of patient information, etc. In this paper, we introduce the BReAst Carcinoma Subtyping
(BRACS) dataset, a large cohort of annotated Hematoxylin & Eosin (H&E)-stained images to advance the characterization
of breast lesions. BRACS contains 547 Whole-Slide Images (WSIs), and 4539 Regions of Interest (RoIs) extracted from the
WSIs. Each WSI, and respective RoIs, are annotated by the consensus of three board-certified pathologists into different lesion
categories. Specifically, BRACS includes three lesion types, i.e., benign, malignant and atypical, which are further subtyped
into seven categories. The included RoIs exhibit large variability in dimensions, and incorporate the usual tissue-preparation
and staining artifacts to bestow a realistic breast cancer diagnosis. It is, to the best of our knowledge, the largest annotated
dataset for breast cancer subtyping both at WSI- and RoI-level. Further, by including the understudied atypical lesions, BRACS
offers an unique opportunity for leveraging AI to better understand their characteristics. We encourage AI practitioners to
develop and evaluate novel algorithms on the BRACS dataset to further breast cancer diagnosis and patient care.



Background & Summary
Histology images contain both complex and ambiguous information, thus challenging pathologists to perform a robust,
reproducible and efficient analysis. Further, histology images are very large, which makes their analysis cumbersome and time-
consuming. With advances in Computer-Aided-Diagnosis (CAD), AI techniques, especially Machine Learning (ML) and DL,
have the potential to address the aforementioned bottlenecks1–4 . These techniques can identify discriminative morphological
patterns from large datasets to diagnose histology images in a standardized and objective manner. However, there exist several
challenges in adopting such techniques in digital pathology, such as, (i) the requirement of large annotated datasets, (ii) the need
for sufficiently variable data to set up cross-patient experiments, (iii) the inclusion of diagnostically challenging lesions, that are
generally difficult and expensive to acquire, (iv) the utilization of sub-region annotations to delineate RoI, (v) the coverage of
diagnostic spectrum, and (vi) coping with data leakage and noisy annotations. Recent advancements in DL have demonstrated
superior capabilities compared to classical ML approaches for CAD5–11 . The crucial advantage of DL approaches is their ability
to learn task-specific salient features directly from the training data. However, this superiority comes at the cost of acquiring
large, high-quality, variable, and unbiased annotated training datasets. Although several datasets for diagnosing breast histology
images exist12–16 , they do not meet all the aforementioned criteria. For instance, some datasets focus on specific diseases that
include only binary classes12, 14 , while others handling multiple classes13, 15 include only a small number of training samples
(both at WSI- and RoI-level) collected from a few patients, thus limiting the dataset variability. Further, the set of considered
classes in a dataset is crucial. Most of the public datasets aim to categorize lesions into benign and malignant classes, which do
not depict the complete spectrum of classes in breast cancer diagnosis. Many of these datasets contain standardized images
without clinical artifacts, e.g., staining anomalies, ink marks, tissue folding, blurred regions, tears etc. Consequently, these
datasets do not comprehensively represent the real-world breast cancer diagnosis. Thus, it is necessary to develop a breast
cancer dataset consisting of heterogeneous images across the diagnostic spectrum which is comparable to real-world diagnosis
performed by the pathologists.
To this end, we introduce BRACS, a large cohort of H&E-stained images to advance CAD of breast lesions. BRACS
features the following advantages over the extant breast cancer image datasets, (i) it includes a large and heterogeneous set of
realistic breast histology images (both at WSI- and RoI-level), (ii) RoIs range over variable dimensions by entirely including
the diagnostic lesion, thus avoiding the loss of diagnostically relevant information, (iii) the images are acquired from a large
number of patients encompassing large variability, and (iv) two atypical lesion categories, also known as precancerous lesions,
are included along with other categories. In particular, we consider the following lesion types, Normal (N), Pathological Benign
(PB), Usual Ductal Hyperplasia (UDH), Flat Epithelial Atypia (FEA), Atypical Ductal Hyperplasia (ADH), Ductal Carcinoma
in Situ (DCIS), and Invasive Carcinoma (IC). Thus, BRACS represents a more realistic benchmark for breast cancer diagnosis
by including several types of typical and atypical tissue samples over a wide variety of WSIs and RoIs extracted from a large
number of patients.


Methods
The BRACS dataset is created to support the development of breast cancer diagnostic methods through the automatic analysis
of histology images. The dataset was developed through the collaboration of the National Cancer Institute - Scientific Institute
for Research, Hospitalization and Healthcare (IRCCS) "Fondazione G. Pascale", the Institute for High Performance Computing
and Networking (ICAR) of National Research Council (CNR), and IBM Research – Zurich. The dataset was acquired from
patients between 2019 and 2020, by board-certified pathologists of the Department of Pathology at the National Cancer Institute
- IRCCS "Fondazione G. Pascale" in Naples (Italy). The samples were generated from H&E-stained breast tissue biopsy slides,
and were selected based on the diagnostic reports of the patients. The age of the patients range from 16 to 86 years, with about
61% of patients in the range of 40-60 years, and only a few patients aging less than 20 years or above 80 years.

WSI- and RoI-level annotations
The curation of rich and comprehensively annotated histology images is a complex and time-consuming task, while being prone
to observer variability. The inclusion of atypical breast lesions at both WSI- and RoI-level further increases the task complexity
by requiring annotations of specialized expert pathologists. Moreover, a WSI typically includes several lesions of different
subtypes. To address the aforementioned challenges, we started by extracting and annotating RoIs in WSIs, and subsequently
derived the WSI-level label as the most severe cancerous lesion detected within the slide. Specifically, the RoI-level annotations
were conducted in a two-step procedure. First, a set of representative RoIs in each WSI was identified. Three board-certified
pathologists independently annotated the RoIs, i.e., either as normal tissue or as one of the six lesion subtypes. Each extracted
RoI corresponds to a unique category, and can include single or multiple glandular structures. Then, the annotations with
disagreement were further discussed and re-annotated by the consensus of three pathologists. This process ensures reliable
annotations, and allows to alleviate inter- and intra-observer variability. Figure 1 presents the annotation procedure for a sample

BRACS: A Dataset for BReAst Carcinoma Subtyping

in H&E Histology Images

Nadia Brancati1* , Anna Maria Anniciello2 , Pushpak Pati3,5 , Daniel Riccio1,4 , Giosuè

Scognamiglio2 , Guillaume Jaume3,6 , Giuseppe De Pietro1 , Maurizio Di Bonito2 , Antonio

Foncubierta3 , Gerardo Botti2 , Maria Gabrani3 , Florinda Feroce2 , and Maria Frucci1

1 Institute for High Performance Computing and Networking of the Research Council of Italy, ICAR-CNR, Naples,

Italy

arXiv:2111.04740v1 [q-bio.QM] 8 Nov 2021

2 National Cancer Institute – IRCCS – Fondazione Pascale, Naples, Italy

3 IBM Research – Zurich, Switzerland

4 University of Naples Federico II, Naples, Italy

5 ETH Zurich, Switzerland

6 EPFL Lausanne, Switzerland

* Corresponding author: nadia.brancati@cnr.it

ABSTRACT

Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women with cancer.

Recent advancements in diagnostic activities combined with large-scale screening policies have significantly lowered the

mortality rates for breast cancer patients. However, the manual inspection of tissue slides by pathologists is cumbersome,

time-consuming, and is subject to significant inter- and intra-observer variability. Recently, the advent of whole-slide scanning

systems have empowered the rapid digitization of pathology slides, and enabled to develop digital workflows. These progress

further enable to leverage Artificial Intelligence (AI) to assist, automate, and augment pathological diagnosis. But AI techniques,

especially Deep Learning (DL), require a large amount of high-quality annotated data to learn from. Constructing such

task-specific datasets poses several challenges, such as, data-acquisition level constrains, time-consuming and expensive

annotations, and anonymization of patient information, etc. In this paper, we introduce the BReAst Carcinoma Subtyping

(BRACS) dataset, a large cohort of annotated Hematoxylin & Eosin (H&E)-stained images to advance the characterization

of breast lesions. BRACS contains 547 Whole-Slide Images (WSIs), and 4539 Regions of Interest (RoIs) extracted from the

WSIs. Each WSI, and respective RoIs, are annotated by the consensus of three board-certified pathologists into different lesion

categories. Specifically, BRACS includes three lesion types, i.e., benign, malignant and atypical, which are further subtyped

into seven categories. The included RoIs exhibit large variability in dimensions, and incorporate the usual tissue-preparation

and staining artifacts to bestow a realistic breast cancer diagnosis. It is, to the best of our knowledge, the largest annotated

dataset for breast cancer subtyping both at WSI- and RoI-level. Further, by including the understudied atypical lesions, BRACS

offers an unique opportunity for leveraging AI to better understand their characteristics. We encourage AI practitioners to

develop and evaluate novel algorithms on the BRACS dataset to further breast cancer diagnosis and patient care.

Background & Summary

Histology images contain both complex and ambiguous information, thus challenging pathologists to perform a robust,

reproducible and efficient analysis. Further, histology images are very large, which makes their analysis cumbersome and time-

consuming. With advances in Computer-Aided-Diagnosis (CAD), AI techniques, especially Machine Learning (ML) and DL,

have the potential to address the aforementioned bottlenecks1–4 . These techniques can identify discriminative morphological

patterns from large datasets to diagnose histology images in a standardized and objective manner. However, there exist several

challenges in adopting such techniques in digital pathology, such as, (i) the requirement of large annotated datasets, (ii) the need

for sufficiently variable data to set up cross-patient experiments, (iii) the inclusion of diagnostically challenging lesions, that are

generally difficult and expensive to acquire, (iv) the utilization of sub-region annotations to delineate RoI, (v) the coverage of

diagnostic spectrum, and (vi) coping with data leakage and noisy annotations. Recent advancements in DL have demonstrated

superior capabilities compared to classical ML approaches for CAD5–11 . The crucial advantage of DL approaches is their ability

to learn task-specific salient features directly from the training data. However, this superiority comes at the cost of acquiring

large, high-quality, variable, and unbiased annotated training datasets. Although several datasets for diagnosing breast histology

images exist12–16 , they do not meet all the aforementioned criteria. For instance, some datasets focus on specific diseases that

include only binary classes12, 14 , while others handling multiple classes13, 15 include only a small number of training samples

(both at WSI- and RoI-level) collected from a few patients, thus limiting the dataset variability. Further, the set of considered

classes in a dataset is crucial. Most of the public datasets aim to categorize lesions into benign and malignant classes, which do

not depict the complete spectrum of classes in breast cancer diagnosis. Many of these datasets contain standardized images

without clinical artifacts, e.g., staining anomalies, ink marks, tissue folding, blurred regions, tears etc. Consequently, these

datasets do not comprehensively represent the real-world breast cancer diagnosis. Thus, it is necessary to develop a breast

cancer dataset consisting of heterogeneous images across the diagnostic spectrum which is comparable to real-world diagnosis

performed by the pathologists.

To this end, we introduce BRACS, a large cohort of H&E-stained images to advance CAD of breast lesions. BRACS

features the following advantages over the extant breast cancer image datasets, (i) it includes a large and heterogeneous set of

realistic breast histology images (both at WSI- and RoI-level), (ii) RoIs range over variable dimensions by entirely including

the diagnostic lesion, thus avoiding the loss of diagnostically relevant information, (iii) the images are acquired from a large

number of patients encompassing large variability, and (iv) two atypical lesion categories, also known as precancerous lesions,

are included along with other categories. In particular, we consider the following lesion types, Normal (N), Pathological Benign

(PB), Usual Ductal Hyperplasia (UDH), Flat Epithelial Atypia (FEA), Atypical Ductal Hyperplasia (ADH), Ductal Carcinoma

in Situ (DCIS), and Invasive Carcinoma (IC). Thus, BRACS represents a more realistic benchmark for breast cancer diagnosis

by including several types of typical and atypical tissue samples over a wide variety of WSIs and RoIs extracted from a large

number of patients.

Methods

The BRACS dataset is created to support the development of breast cancer diagnostic methods through the automatic analysis

of histology images. The dataset was developed through the collaboration of the National Cancer Institute - Scientific Institute

for Research, Hospitalization and Healthcare (IRCCS) "Fondazione G. Pascale", the Institute for High Performance Computing

and Networking (ICAR) of National Research Council (CNR), and IBM Research – Zurich. The dataset was acquired from

patients between 2019 and 2020, by board-certified pathologists of the Department of Pathology at the National Cancer Institute

- IRCCS "Fondazione G. Pascale" in Naples (Italy). The samples were generated from H&E-stained breast tissue biopsy slides,

and were selected based on the diagnostic reports of the patients. The age of the patients range from 16 to 86 years, with about

61% of patients in the range of 40-60 years, and only a few patients aging less than 20 years or above 80 years.

WSI- and RoI-level annotations

The curation of rich and comprehensively annotated histology images is a complex and time-consuming task, while being prone

to observer variability. The inclusion of atypical breast lesions at both WSI- and RoI-level further increases the task complexity

by requiring annotations of specialized expert pathologists. Moreover, a WSI typically includes several lesions of different

subtypes. To address the aforementioned challenges, we started by extracting and annotating RoIs in WSIs, and subsequently

derived the WSI-level label as the most severe cancerous lesion detected within the slide. Specifically, the RoI-level annotations

were conducted in a two-step procedure. First, a set of representative RoIs in each WSI was identified. Three board-certified

pathologists independently annotated the RoIs, i.e., either as normal tissue or as one of the six lesion subtypes. Each extracted

RoI corresponds to a unique category, and can include single or multiple glandular structures. Then, the annotations with

disagreement were further discussed and re-annotated by the consensus of three pathologists. This process ensures reliable

annotations, and allows to alleviate inter- and intra-observer variability. Figure 1 presents the annotation procedure for a sample