{"url":"/dataset/pcam","name":"PCam","full_name":"PatchCamelyon","description_markdown":"**PatchCamelyon** is an image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annotated with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than ImageNet, trainable on a single GPU.","description_withheld":null,"homepage":"https://github.com/basveeling/pcam","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/rotation-equivariant-cnns-for-digital","title":"Rotation Equivariant CNNs for Digital Pathology","first_author":"Bastiaan S. Veeling","url":null},"license":{"name":"CC0","url":"https://choosealicense.com/licenses/cc0-1.0/"},"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":"Image Clustering","url":"/task/image-clustering","datasets_with_task":"/datasets/task/image-clustering"},{"name":"Image Compression","url":"/task/image-compression","datasets_with_task":"/datasets/task/image-compression"},{"name":"Breast Tumour Classification","url":"/task/breast-tumour-classification","datasets_with_task":"/datasets/task/breast-tumour-classification"}],"languages":[],"variants":["PCam"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/1aurent/PatchCamelyon","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/pytorch/vision","url":"https://pytorch.org/vision/stable/generated/torchvision.datasets.PCAM.html","frameworks":["pytorch"]},{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/patch_camelyon","frameworks":["tf","jax"]},{"repo":"https://github.com/basveeling/pcam","url":"https://github.com/basveeling/pcam","frameworks":[]}],"num_papers_in_archive":110,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/breast-tumour-classification-on-pcam","task":"Breast Tumour Classification","dataset_variant":"PCam","rows":16,"metrics":["AUC","Accuracy"],"first_row_in_archive_order":{"model":"DSF-CNN (C8)","paper":"/paper/dense-steerable-filter-cnns-for-exploiting","metrics":{"AUC":"0.975"},"code_links":[{"title":"simongraham/dsf-cnn","url":"https://github.com/simongraham/dsf-cnn"},{"title":"ladislasl/CNN_invar_rot","url":"https://github.com/ladislasl/CNN_invar_rot"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-clustering-on-pcam","task":"Image Clustering","dataset_variant":"PCam","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"TURTLE (CLIP + DINOv2)","paper":"/paper/let-go-of-your-labels-with-unsupervised-1","metrics":{"Accuracy":"52.0"},"code_links":[{"title":"mlbio-epfl/turtle","url":"https://github.com/mlbio-epfl/turtle"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-compression-on-pcam","task":"Image Compression","dataset_variant":"PCam","rows":1,"metrics":["Bit rate"],"first_row_in_archive_order":{"model":"Lossyless Compressor","paper":"/paper/lossy-compression-for-lossless-prediction","metrics":{"Bit rate":"1490"},"code_links":[{"title":"YannDubs/lossyless","url":"https://github.com/YannDubs/lossyless"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-classification-on-pcam","task":"Image Classification","dataset_variant":"PCam","rows":0,"metrics":["Accuracy"],"first_row_in_archive_order":null,"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/let-go-of-your-labels-with-unsupervised-1","title":"Let Go of Your Labels with Unsupervised Transfer","date":"2024-06-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/virchow-a-million-slide-digital-pathology","title":"Virchow: A Million-Slide Digital Pathology Foundation Model","date":"2023-09-14","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":9,"samples_unverified":0,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/lossy-compression-for-lossless-prediction","title":"Lossy Compression for Lossless Prediction","date":"2021-06-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":2,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dense-steerable-filter-cnns-for-exploiting","title":"Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images","date":"2020-04-06","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/roto-translation-equivariant-convolutional","title":"Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis","date":"2020-02-20","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/rotation-equivariant-cnns-for-digital","title":"Rotation Equivariant CNNs for Digital Pathology","date":"2018-06-08","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/learning-steerable-filters-for-rotation","title":"Learning Steerable Filters for Rotation Equivariant CNNs","date":"2017-11-20","rows_on_this_dataset":4,"code_links":0,"syntology":null},{"paper":"/paper/rotation-equivariant-vector-field-networks","title":"Rotation equivariant vector field networks","date":"2016-12-29","rows_on_this_dataset":3,"code_links":3,"syntology":null},{"paper":"/paper/densely-connected-convolutional-networks","title":"Densely Connected Convolutional Networks","date":"2016-08-25","rows_on_this_dataset":1,"code_links":146,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":71,"samples_ran":18,"samples_unverified":53,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/group-equivariant-convolutional-networks","title":"Group Equivariant Convolutional Networks","date":"2016-02-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","rows_on_this_dataset":2,"code_links":484,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":377,"samples_ran":230,"samples_unverified":147,"pointer_only_for_licence":187,"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":7,"samples_harvested":479,"samples_ran":263,"samples_unverified":216,"pointer_only_for_licence":207,"papers_with_no_sample_that_ran":1,"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."}