  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.
