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.

本文介绍了 BReAst Carcinoma Subtyping（BRACS）数据集，这是一个包含 547 张 WSI 和 4539 个感兴趣区域（ROIs）的大型数据集，可以帮助进一步了解乳腺癌的特征以及利用人工智能技术对其进行诊断和分类。