{"url":"/dataset/camelyon16","name":"CAMELYON16","full_name":"Cancer Metastases in Lymph Nodes Challenge 2016","description_markdown":"The dataset consists of 400 whole-slide images (WSIs) of lymph node sections stained with hematoxylin and eosin (H&E), collected from two medical centers in the Netherlands. The WSIs are stored in a multi-resolution pyramid format, allowing for efficient retrieval of image subregions at different magnification levels. The training set includes two subsets: \r\n\r\n- 170 WSIs (100 normal, 70 with metastases) from Radboud University Medical Center\r\n- 100 WSIs (60 normal, 40 with metastases) from University Medical Center Utrecht\r\n\r\nThe test set consists of 130 WSIs from both institutions. Ground truth data for metastases is provided as XML files with annotated contours and WSI binary masks.\r\n\r\n\r\nThe Camelyon16 dataset aims to reduce the workload and subjectivity in cancer diagnosis by pathologists. It serves as a benchmark for evaluating algorithms that can automatically detect metastases in histopathological images, focusing on breast cancer in sentinel lymph nodes.\r\n\r\n\r\nResearchers can develop and refine machine learning models for automated detection of metastases. The dataset allows for performance comparisons of different detection algorithms. Automated systems can be integrated into clinical workflows to enhance diagnostic accuracy and efficiency. The dataset is valuable for training medical professionals in digital pathology and AI applications in diagnostics.","description_withheld":null,"homepage":"https://camelyon16.grand-challenge.org/","introduced_date":"2017-12-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/diagnostic-assessment-of-deep-learning","title":"Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer","first_author":"Babak Ehteshami Bejnordi","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Multiple Instance Learning","url":"/task/multiple-instance-learning","datasets_with_task":"/datasets/task/multiple-instance-learning"},{"name":"Breast Cancer Detection","url":"/task/breast-cancer-detection","datasets_with_task":"/datasets/task/breast-cancer-detection"},{"name":"whole slide images","url":"/task/whole-slide-images","datasets_with_task":"/datasets/task/whole-slide-images"}],"languages":[],"variants":["CAMELYON16"],"data_loaders":[],"num_papers_in_archive":172,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/multiple-instance-learning-on-camelyon16","task":"Multiple Instance Learning","dataset_variant":"CAMELYON16","rows":14,"metrics":["AUC","ACC","Expected Calibration Error","FROC","Patch AUC"],"first_row_in_archive_order":{"model":"Snuffy (DINO Exhaustive)","paper":"/paper/snuffy-efficient-whole-slide-image-classifier","metrics":{"ACC":"0.948","AUC":"0.987","Expected Calibration Error":"0.083","FROC":"0.675","Patch AUC":"0.957"},"code_links":[{"title":"jafarinia/snuffy","url":"https://github.com/jafarinia/snuffy"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/snuffy-efficient-whole-slide-image-classifier","title":"Snuffy: Efficient Whole Slide Image Classifier","date":"2024-08-15","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/camil-context-aware-multiple-instance","title":"CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images","date":"2023-05-09","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/dgmil-distribution-guided-multiple-instance","title":"DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification","date":"2022-06-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dtfd-mil-double-tier-feature-distillation","title":"DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification","date":"2022-03-22","rows_on_this_dataset":4,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":2,"samples_unverified":4,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/transmil-transformer-based-correlated","title":"TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification","date":"2021-06-02","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/dual-stream-multiple-instance-learning","title":"Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning","date":"2020-11-17","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":4,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":15,"samples_ran":9,"samples_unverified":6,"pointer_only_for_licence":6,"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."}