{"url":"/dataset/kumar","name":"Kumar","full_name":null,"description_markdown":"The **Kumar** dataset contains 30 1,000×1,000 image tiles from seven organs (6 breast, 6 liver, 6 kidney, 6 prostate, 2 bladder, 2 colon and 2 stomach) of The Cancer Genome Atlas (TCGA) database acquired at 40× magnification. Within each image, the boundary of each nucleus is fully annotated.\r\n\r\nSource: [Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images](https://arxiv.org/abs/2004.03037)","description_withheld":null,"homepage":"https://monuseg.grand-challenge.org/Data/","introduced_date":"2022-06-19","introduced_date_note":null,"introduced_by":{"paper":"/paper/0-1-deep-neural-networks-via-block-coordinate","title":"0/1 Deep Neural Networks via Block Coordinate Descent","first_author":"HUI ZHANG","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Interactive","url":"/datasets/modality/interactive"}],"tasks":[{"name":"Multi-tissue Nucleus Segmentation","url":"/task/multi-tissue-nucleus-segmentation","datasets_with_task":"/datasets/task/multi-tissue-nucleus-segmentation"}],"languages":[],"variants":["Kumar"],"data_loaders":[],"num_papers_in_archive":17,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/multi-tissue-nucleus-segmentation-on-kumar","task":"Multi-tissue Nucleus Segmentation","dataset_variant":"Kumar","rows":18,"metrics":["Dice","Hausdorff Distance (mm)","Jaccard Index","PQ"],"first_row_in_archive_order":{"model":"GC-MHVN","paper":"/paper/mrl-learning-to-mix-with-attention-and","metrics":{"Dice":"0.843","Jaccard Index":"0.652","PQ":"0.625"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/mrl-learning-to-mix-with-attention-and","title":"MRL: Learning to Mix with Attention and Convolutions","date":"2022-08-30","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"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/cia-net-robust-nuclei-instance-segmentation","title":"CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation","date":"2019-03-13","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/xy-network-for-nuclear-segmentation-in-multi","title":"HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images","date":"2018-12-16","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":1,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/micro-net-a-unified-model-for-segmentation-of","title":"Micro-Net: A unified model for segmentation of various objects in microscopy images","date":"2018-04-22","rows_on_this_dataset":1,"code_links":0,"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/mask-r-cnn","title":"Mask R-CNN","date":"2017-03-20","rows_on_this_dataset":1,"code_links":179,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":140,"samples_ran":42,"samples_unverified":98,"pointer_only_for_licence":23,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/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/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","rows_on_this_dataset":1,"code_links":487,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":757,"samples_ran":510,"samples_unverified":247,"pointer_only_for_licence":426,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fully-convolutional-networks-for-semantic-1","title":"Fully Convolutional Networks for Semantic Segmentation","date":"2014-11-14","rows_on_this_dataset":1,"code_links":51,"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."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":6,"samples_harvested":914,"samples_ran":557,"samples_unverified":357,"pointer_only_for_licence":453,"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."}