# Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review

arXiv: https://arxiv.org/abs/2306.01546 · HTML: https://mlsanity.com/paper/2306.01546/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 215 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 1 of 1)

- [Weighted multi-level deep learning analysis and framework for processing breast cancer WSIs](https://mlsanity.com/paper/2106.14708/) (2021)

## Built on by (most cited first, 5 of 5)

- [Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images](https://mlsanity.com/paper/2409.09053/) (2024)
- [A Unified Low-level Foundation Model for Enhancing Pathology Image Quality](https://mlsanity.com/paper/2509.01071/) (2025)
- [Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability](https://mlsanity.com/paper/2406.12897/) (2024)
- [Open-source framework for detecting bias and overfitting for large pathology images](https://mlsanity.com/paper/2503.01827/) (2025)
- [Dataset creation for supervised deep learning-based analysis of microscopic images -- review of important considerations and recommendations](https://mlsanity.com/paper/2512.04564/) (2025)

## Abstract (arXiv)

Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E stained whole-slide images (WSI) that can be used to develop deep learning algorithms. We systematically searched nine scientific literature databases and nine research data repositories and found 17 publicly available datasets containing 10385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled two lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata.