{"url":"/dataset/breakhis","name":"BreakHis","full_name":"Breast Cancer Histopathological Database","description_markdown":"The Breast Cancer Histopathological Image Classification (BreakHis) is  composed of 9,109 microscopic images of breast tumor tissue collected from 82 patients using different magnifying factors (40X, 100X, 200X, and 400X).  It contains 2,480  benign and 5,429 malignant samples (700X460 pixels, 3-channel RGB, 8-bit depth in each channel, PNG format). This database has been built in collaboration with the P&D Laboratory - Pathological Anatomy and Cytopathology, Parana, Brazil.\r\n\r\nPaper: [F. A. Spanhol, L. S. Oliveira, C. Petitjean and L. Heutte, \"A Dataset for Breast Cancer Histopathological Image Classification,\" in IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1455-1462, July 2016, doi: 10.1109/TBME.2015.2496264](https://doi.org/10.1109/TBME.2015.2496264)\r\n\r\nSource: [https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/](https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/)\r\n\r\nImage Source: [https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/](https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/)","description_withheld":null,"homepage":"https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Breast Cancer Histology Image Classification","url":"/task/breast-cancer-histology-image-classification","datasets_with_task":"/datasets/task/breast-cancer-histology-image-classification"},{"name":"Breast Cancer Detection","url":"/task/breast-cancer-detection","datasets_with_task":"/datasets/task/breast-cancer-detection"},{"name":"Medical Image Retrieval","url":"/task/medical-image-retrieval","datasets_with_task":"/datasets/task/medical-image-retrieval"},{"name":"Breast Cancer Histology Image Classification (20% labels)","url":"/task/breast-cancer-histology-image-classification-1","datasets_with_task":"/datasets/task/breast-cancer-histology-image-classification-1"}],"languages":[],"variants":["BreakHis"],"data_loaders":[{"repo":"https://github.com/niconaufal21/cnn-breast-cancer","url":"https://github.com/niconaufal21/cnn-breast-cancer","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/breast-cancer-histology-image-classification","task":"Breast Cancer Histology Image Classification","dataset_variant":"BreakHis","rows":5,"metrics":["Accuracy (%)","1:1 Accuracy","Accuracy (Inter-Patient)"],"first_row_in_archive_order":{"model":"WaveMix","paper":"/paper/which-backbone-to-use-a-resource-efficient","metrics":{"Accuracy (%)":"99.39"},"code_links":[{"title":"pranavphoenix/Backbones","url":"https://github.com/pranavphoenix/Backbones"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-classification-on-breakhis","task":"Image Classification","dataset_variant":"BreakHis","rows":3,"metrics":["Average Test Accuracy over all magnifications"],"first_row_in_archive_order":{"model":"WaveMix","paper":"/paper/which-backbone-to-use-a-resource-efficient","metrics":{"Average Test Accuracy over all magnifications":"99.39"},"code_links":[{"title":"pranavphoenix/Backbones","url":"https://github.com/pranavphoenix/Backbones"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/breast-cancer-detection-on-breakhis","task":"Breast Cancer Detection","dataset_variant":"BreakHis","rows":2,"metrics":["1:1 Accuracy"],"first_row_in_archive_order":{"model":"IRv2-CXL","paper":"/paper/classification-of-breast-tumours-based-on","metrics":{"1:1 Accuracy":"96.46"},"code_links":[{"title":"mohammadAbbasniya/BreastCancer-Classification","url":"https://github.com/mohammadAbbasniya/BreastCancer-Classification"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/breast-cancer-histology-image-classification-1","task":"Breast Cancer Histology Image Classification (20% labels)","dataset_variant":"BreakHis","rows":1,"metrics":["1:1 Accuracy","Accuracy (Inter-Patient)"],"first_row_in_archive_order":{"model":"EfficientNet-b2","paper":"/paper/magnification-prior-a-self-supervised-method","metrics":{"1:1 Accuracy":"88.77","Accuracy (Inter-Patient)":"88.77"},"code_links":[{"title":"prakashchhipa/magnification-prior-self-supervised-method","url":"https://github.com/prakashchhipa/magnification-prior-self-supervised-method"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/medical-image-retrieval-on-breakhis","task":"Medical Image Retrieval","dataset_variant":"BreakHis","rows":1,"metrics":["Average Precision"],"first_row_in_archive_order":{"model":"CNN AutoEncoder","paper":"/paper/cnn-based-autoencoder-application-in-breast","metrics":{"Average Precision":"0.9237"},"code_links":[{"title":"forderation/breast-cancer-retrieval","url":"https://github.com/forderation/breast-cancer-retrieval"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/breast-net-a-lightweight-dcnn-model-for","title":"Breast-NET: a lightweight DCNN model for breast cancer detection and grading using histological samples","date":"2024-08-10","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/which-backbone-to-use-a-resource-efficient","title":"Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision","date":"2024-06-09","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/magnification-invariant-medical-image","title":"Magnification Invariant Medical Image Analysis: A Comparison of Convolutional Networks, Vision Transformers, and Token Mixers","date":"2023-02-22","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/classification-of-breast-tumours-based-on","title":"Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods","date":"2022-09-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/vggin-net-deep-transfer-network-for","title":"VGGIN-Net: Deep Transfer Network for Imbalanced Breast Cancer Dataset","date":"2022-03-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/magnification-prior-a-self-supervised-method","title":"Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images","date":"2022-03-15","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/cnn-based-autoencoder-application-in-breast","title":"CNN Based Autoencoder Application in Breast Cancer Image Retrieval","date":"2021-08-04","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}