mlsanityData to 7 Oct 2026

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

arXiv 2111.04740 ↗First posted 8 Nov 2021q-bio.QM cs.AI cs.CVcited by 130

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Abstract (arXiv)

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.

Key facts. Cited by 130 papers in the map (data through 7 Oct 2026). Builds on 10 of its 48 references that are in the map. First posted on arXiv 8 Nov 2021.

Builds on

10 of 48 references.

References · 10

  1. 2019Deep neural network models for computational histopathology: A surveycited by 165
  2. 2018BACH: Grand Challenge on Breast Cancer Histology Imagescited by 131
  3. 2018Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challengecited by 61
  4. 2021Hierarchical Graph Representations in Digital Pathologycited by 58
  5. 2020Quantifying Explainers of Graph Neural Networks in Computational Pathologycited by 35
  6. 2020HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classificationcited by 33
  7. 2020Towards Explainable Graph Representations in Digital Pathologycited by 17
  8. 2021Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendationscited by 13
  9. 2021Machine Learning Methods for Histopathological Image Analysis: A Reviewcited by 10
  10. 2021Gigapixel Histopathological Image Analysis using Attention-based Neural Networkscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 17; 2024: 29; 2025: 40; 2026: 41.

130 papers.

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  1. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 357
  2. 2024HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysiscited by 104
  3. 2024MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathologycited by 87
  4. 2023Attention-Challenging Multiple Instance Learning for Whole Slide Image Classificationcited by 61
  5. 2022Differentiable Zooming for Multiple Instance Learning on Whole-Slide Imagescited by 43
  6. 2023A General-Purpose Self-Supervised Model for Computational Pathologycited by 42
  7. 20242DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classificationcited by 35
  8. 2024Interactive Medical Image Segmentation: A Benchmark Dataset and Baselinecited by 30
  9. 2024PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaborationcited by 28
  10. 2025Do Multiple Instance Learning Models Transfer?cited by 28
  11. 2025Evidence-based diagnostic reasoning with multi-agent copilot for human pathologycited by 26
  12. 2023ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysiscited by 25
38 more
  1. 2025Training state-of-the-art pathology foundation models with orders of magnitude less datacited by 19
  2. 2024A Survey on Mixup Augmentations and Beyondcited by 18
  3. 2024Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarioscited by 16
  4. 2023Prompt-MIL: Boosting Multi-Instance Learning Schemes via Task-specific Prompt Tuningcited by 15
  5. 2023Rotation-Agnostic Image Representation Learning for Digital Pathologycited by 15
  6. 2023Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole Slide Image Classificationcited by 12
  7. 2024MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Modelscited by 12
  8. 2024Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysiscited by 12
  9. 2024HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classificationcited by 12
  10. 2025A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Modelcited by 12
  11. 2025CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Losscited by 11
  12. 2025PixCell: A generative foundation model for digital histopathology imagescited by 11
  13. 2023Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compressioncited by 10
  14. 2024A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Modelscited by 9
  15. 2023Generative appearance replay for continual unsupervised domain adaptationcited by 8
  16. 2024Analysis and Validation of Image Search Engines in Histopathologycited by 7
  17. 2024RetMIL: Retentive Multiple Instance Learning for Histopathological Whole Slide Image Classificationcited by 7
  18. 2024MergeUp-augmented Semi-Weakly Supervised Learning for WSI Classificationcited by 7
  19. 2024Tissue Concepts: supervised foundation models in computational pathologycited by 7
  20. 2025How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmarkcited by 7
  21. 2023MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysiscited by 6
  22. 2024Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classificationcited by 6
  23. 2025Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representationcited by 6
  24. 2025Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Studycited by 6
  25. 2024cDP-MIL: Robust Multiple Instance Learning via Cascaded Dirichlet Processcited by 5
  26. 2024Continual Domain Incremental Learning for Privacy-aware Digital Pathologycited by 5
  27. 2026ASMIL: Attention-Stabilized Multiple Instance Learning for Whole Slide Imagingcited by 5
  28. 2026MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflowscited by 5
  29. 2022Stain-invariant self supervised learning for histopathology image analysiscited by 4
  30. 2023Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learningcited by 4
  31. 2023RoFormer for Position Aware Multiple Instance Learning in Whole Slide Image Classificationcited by 4
  32. 2024Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathologycited by 4
  33. 2024Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Imagecited by 4
  34. 2024Gen-SIS: Generative Self-augmentation Improves Self-supervised Learningcited by 4
  35. 2025Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathologycited by 4
  36. 2025Enhancing Zero-Shot Brain Tumor Subtype Classification via Fine-Grained Patch-Text Alignmentcited by 4
  37. 2025Ensemble learning of pathology foundation models for precision oncologycited by 4
  38. 2025Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddingscited by 4

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