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BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images

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 the 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 advances further enable to leverage Artificial Intelligence (AI) to assist, automate, and augment pathological diagnosis. But the 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 private information. In this paper, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of annotated Hematoxylin&Eosin (H&E)-stained images to facilitate 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. 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.


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 timeconsuming. With advances in Computer-Aided-Diagnosis (CAD), AI techniques, especially Machine Learning (ML) and DL, have the potential to address the aforementioned bottlenecks [1][2][3][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 CAD [5][6][7][8][9][10][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 exist [12][13][14][15][16] , they do not meet all the aforementioned criteria. For instance, some datasets focus on specific diseases that include only binary classes 12,14 , while others handling multiple classes 13,15 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 WSI and its corresponding RoIs. All the RoIs were extracted and annotated with the help of QuPath 17 . The number of extracted RoIs per WSI ranges from 0 to 119, with an average of 11 RoIs per WSI. Multiple RoIs corresponding to different lesion subtypes were selected per WSI such that the collected RoIs across all WSIs collectively encompass the lesion heterogeneity. This aspect is very crucial for representing pathogenesis and disease progression, and consequently allowing for the inclusion of sufficiently variable data for DL model training. Subsequently, the WSI-level labels were trivially defined as the most aggressive tumor subtype annotated in the image.

Subtype annotations
An important characteristics of BRACS is the inclusion of atypical lesions, ADH and FEA. While ignored in other public datasets, these categories remain important as they might be indicators of either (i) the presence of abnormalities in neighboring breast tissue that could go undetected, e.g., due to the extraction of small tissue samples, or (ii) a high risk of onset of future carcinoma, i.e., development of DCIS and IC. In addition, these lesions cannot be detected by mammography or other breast imaging techniques, nor can they be felt during a clinical breast examination. When detected in a core biopsy, more frequent imaging follow-up and often surgical excision are recommended 18 . BRACS also includes lesion subtypes belonging to benign and malignant types. In particular, benign lesions are subtyped as either non-cancerous lesions (PB) or inflammatory responses In order to clarify the description of the different tissue subtypes, a brief description of the mammary gland should be considered. The breast is a modified apocrine sweat gland, made up of 15-25 independent glandular units called lobes, each of which is formed by a compound tubulo-acinar gland. The lobes are comprised of adipose tissue and divided by connective tissue septa. Inside each lobe, the main ducts branch into terminal ducts, each of which leads to a lobule that is made up of many berries to form the ductulo-lobular terminal unit. Detailed information on lesions included in BRACS can be found in 19 . The specific features of the different sample tissue subtypes are shortly summarized in the following and a representative example for each of them is shown in Figure 2.

Normal Tissue (N)
In normal mammary glandular tissue, there are two types of epithelial cells (the luminal layer and the basal myoepithelial layer) and two types of stromal cells (interlobular stroma and intralobular stroma). Differently from PB, the ratio between epytelial component and stroma is preserved.

Pathological Benign (PB)
Benign breast lesions can be grouped according to the risk of developing invasive carcinoma and include several groups of histological entities classified in relation to morphology. In our study, for reasons of differential diagnosis, in the PB category we included both non-proliferative lesions and proliferative lesions with the exception of UDH, FEA and ADH, which were considered as three independent subtypes. Therefore, PB includes cyst, apocrine metaplasia, ductal ectasia, squamous metaplasia, atrophy, stromal fibrosis, mastitis, sclerosing adenosis, papilloma, radial scar, simple and complex fibroadenoma.

Usual Ductal Hyperplasia (UDH)
UDH is characterized by an increase in the epithelial layers. It is a cohesive proliferation of disorderly distributed but oriented cells. It can have different architectural aspects (solid pattern, fenestrated pattern, micropapillary pattern). Even if UDH shares some architectural features with ADH and DCIS, it does not show atypia.

Flat Epithelial Atypia (FEA)
FEA is a proliferative lesion characterized by low grade cytological atypia, cell monomorphism, loss of polarity and orientation with respect to the basement membrane, presence of apical snout, endoluminal secretion and frequent calcifications.

Atypical Ductal Hyperplasia (ADH)
ADH is a proliferation of monomorphic cells, which only partially fill the ductal spaces. Architectural aspects include a solid pattern, a cribriform pattern and a papillary pattern. The cytologic atypia is similar to that of low-grade DCIS, but the lesion

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spans no more than 2mm or has an insufficient architectural atypia involving only partially ducts and / or lobules.

Ductal Carcinoma in Situ (DCIS)
In situ carcinoma is a malignant proliferation of epithelial cells that fills the entire duct, without evidence of stroma invasion. Typically it involves multiple adjacent ductal spaces. It can have cribriform, solid, papillary and micropapillary patterns.

Invasive Carcinoma (IC)
IC is characterized by the invasion of tumor cells infiltrating the breast stroma with loss of peripheral myoepithelial cells. The presence of the myoepithelial cell layer is an important distinction of DCIS from IC.
Detecting certain subtypes is particularly challenging as some morphological patterns can be shared by several classes. For instance, ADH shares morphological similarities with DCIS. In certain cases, it even includes all the features of DCIS, but is simply limited in size. Also, UDH, ADH and DCIS are all characterized by an intraductal growth pattern, which makes these classes difficult to classify and differentiate in H&E-stained sections. This is particularly true when pathologists cannot utilize significant WSI-level context. We emphasize that when classifying BRACS RoIs, no context information is provided beside the tissue lesion and immediate tissue micro-environment.

BRACS dataset characteristics
The BRACS dataset contains 547 WSIs extracted on 189 different patients. It also includes 4'539 RoIs extracted from 387 WSI collected on 151 patients. All slides were scanned with an Aperio AT2 scanner at 0.25 µm/pixel using a magnification factor of 40×. Table 1 and Table 2

Comparison with existing datasets
In recent years, several data sets have been proposed, with more and more samples and classes [20][21][22][23][24][25] . Table 3 details existing public datasets of histology images for breast lesion classification. Datasets that are (i) either no more accessible 21 , (ii) subsets of already mentioned datasets 22 , or (iii) targeting specific tasks, e.g., lesion proliferation scores prediction 24 , pN-stage prediction 25 , are not mentioned in Table 3. The IDC 12 and Camelyon16 23 datasets are focusing on the detection of the presence of a given lesion. In particular, IDC provides RoIs at small spatial resolution (50 × 50 pixels) extracted from large areas of Invasive Ductal Carcinoma. We emphasize that even if the number of RoIs in BRACS is lower than in IDC, BRACS images are on average much larger allowing the inclusion of whole glandular areas. BRACS includes a larger number of WSIs than Camelyon16 and more subtypes. BreakHis 26 and BACH 20 datasets are devoted to multi-classification tasks, but remain significantly smaller than BRACS, both in terms of image size and number of samples, and include less subtypes than BRACS.
In conclusion, the number of WSIs, RoIs and associated patients, is significantly larger in BRACS compared to the existing public datasets. Moreover, BreakHis and BACH include fixed-size RoIs, while BRACS images are of arbitrary size. Assuming fixed-size tumor regions is a strong assumption that does not apply in real-life scenarios. Limiting the size of RoIs imposes to either (i) partially cut the lesion, hence producing a loss of information that could be pivotal for a correct diagnosis, or (ii) manually curating RoIs such that they all have similar sizes, which does not encompass tumor heterogeneity. By proposing

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Dataset, Year   Table 4. General information on BRACS dataset samples of varying sizes, BRACS promotes the development of DL algorithms that need to be able to operate on inputs of different dimensionality. BRACS is also the first dataset to include pre-cancerous atypical lesions. BRACS and BACH share the same malignant lesion subtypes, while BreakHis refines this class by partitioning it into four specific subtypes. All the benign lesion subtypes defined in BACH and BreakHis are also included in BRACS (normal and benign). In addition, BRACS includes UDH lesion subtype that is not considered in BACH and BreakHis.
In summary, BRACS characteristics are unique, as it allows for multi-class classification task of breast cancer lesions, including challenging atypical lesions. BRACS is also larger than the existing datasets in terms of number RoIs, WSIs and patients.

Dataset splitting
To foster reproducibility and following ML best practices, we provide pre-defined WSI-and RoI-level splits in training, validation and test sets. Data split was generated such that all the WSIs extracted from a patient belong to the same set. Similarly, all the RoIs extracted in a given WSI are assigned to the same split. By following this approach, we avoid that different sets include correlated patient-and slide-level information, that could be leading to overly optimistic prediction results 28 . Table 5 and Table 6 present the number of WSIs included in the train, validation, and test splits. Information about the number of patients in each set is provided in Table 5. Equivalent information for RoIs is shown in Table 7 and Table 8. At RoI-level, the ratio between the most common subtype (PB with 714 samples) and the least common one (N with 357) is around two. At WSI-level, the most common subtype is PB with 120 samples and the least common one is FEA with 24 samples, hence of ratio of around four. Considering the constraint of patient-and WSI-level split, and the fact that atypical lesions are more rare than malignant ones, BRACS offers a rather balanced set, that can directly be used for training DL systems. Train  203  52  140  395  133  Validation  30  14  21  67  25  Test  32  23  32  85  31   Table 5. WSI-level split according to the lesion type.  Train  27 120  56  24  28  40  100  Validation 10 11  9  6  8  9  12  Test  7  16  9  11  12 12 20