{"url":"/sota/multi-tissue-nucleus-segmentation-on-kumar","task":{"name":"Multi-tissue Nucleus Segmentation","url":"/task/multi-tissue-nucleus-segmentation","note":null},"dataset":{"name":"Kumar","url":"/dataset/kumar"},"category":"Medical","categories":["Medical"],"category_note":null,"description":null,"description_from":null,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Dice","Hausdorff Distance (mm)","Jaccard Index","PQ"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Dice":"higher","Hausdorff Distance (mm)":"lower","Jaccard Index":null,"PQ":null}},"counts":{"rows":18,"rows_with_code":11,"rows_with_paper_page":18,"rows_dated":18,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"GC-MHVN","metrics":{"Dice":"0.843","Jaccard Index":"0.652","PQ":"0.625"},"uses_additional_data":false,"paper_date":"2022-08-30","paper":"/paper/mrl-learning-to-mix-with-attention-and","paper_url":"https://arxiv.org/abs/2208.13975v1","paper_title":"MRL: Learning to Mix with Attention and Convolutions","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"DSF-CNN (C8)","metrics":{"Dice":"0.826","Hausdorff Distance (mm)":"60"},"uses_additional_data":false,"paper_date":"2020-04-06","paper":"/paper/dense-steerable-filter-cnns-for-exploiting","paper_url":"https://arxiv.org/abs/2004.03037v2","paper_title":"Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images","code":"https://github.com/simongraham/dsf-cnn","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"HoVer-Net (e)","metrics":{"Dice":"0.826","Hausdorff Distance (mm)":"59.7"},"uses_additional_data":false,"paper_date":"2018-12-16","paper":"/paper/xy-network-for-nuclear-segmentation-in-multi","paper_url":"https://arxiv.org/abs/1812.06499v5","paper_title":"HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images","code":"https://github.com/vqdang/xy_net","n_code_links":4,"syntology":{"n_ran":1,"n_unverified":5,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"Steerable G-CNN (C12)","metrics":{"Dice":"0.820","Hausdorff Distance (mm)":"55.8"},"uses_additional_data":false,"paper_date":"2017-11-20","paper":"/paper/learning-steerable-filters-for-rotation","paper_url":"http://arxiv.org/abs/1711.07289v3","paper_title":"Learning Steerable Filters for Rotation Equivariant CNNs","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":5,"model":"CIA-Net (e)","metrics":{"Dice":"0.818","Hausdorff Distance (mm)":"57.7"},"uses_additional_data":false,"paper_date":"2019-03-13","paper":"/paper/cia-net-robust-nuclei-instance-segmentation","paper_url":"http://arxiv.org/abs/1903.05358v1","paper_title":"CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":6,"model":"Steerable G-CNN (C12)","metrics":{"Dice":"0.818","Hausdorff Distance (mm)":"54.3"},"uses_additional_data":false,"paper_date":"2017-11-20","paper":"/paper/learning-steerable-filters-for-rotation","paper_url":"http://arxiv.org/abs/1711.07289v3","paper_title":"Learning Steerable Filters for Rotation Equivariant CNNs","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":7,"model":"G-CNN (C12)","metrics":{"Dice":"0.814","Hausdorff Distance (mm)":"53.4"},"uses_additional_data":false,"paper_date":"2020-02-20","paper":"/paper/roto-translation-equivariant-convolutional","paper_url":"https://arxiv.org/abs/2002.08725v1","paper_title":"Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis","code":"https://github.com/tueimage/se2cnn","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":6,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":8,"model":"VF-CNN (C12)","metrics":{"Dice":"0.813","Hausdorff Distance (mm)":"51.4"},"uses_additional_data":false,"paper_date":"2016-12-29","paper":"/paper/rotation-equivariant-vector-field-networks","paper_url":"http://arxiv.org/abs/1612.09346v3","paper_title":"Rotation equivariant vector field networks","code":"https://github.com/COGMAR/RotEqNet","n_code_links":3,"syntology":null},{"rank_in_archive_order":9,"model":"G-CNN (C12)","metrics":{"Dice":"0.811","Hausdorff Distance (mm)":"51.9"},"uses_additional_data":false,"paper_date":"2020-02-20","paper":"/paper/roto-translation-equivariant-convolutional","paper_url":"https://arxiv.org/abs/2002.08725v1","paper_title":"Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis","code":"https://github.com/tueimage/se2cnn","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":6,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":10,"model":"Steerable G-CNN (C4)","metrics":{"Dice":"0.809","Hausdorff Distance (mm)":"54.2"},"uses_additional_data":false,"paper_date":"2017-11-20","paper":"/paper/learning-steerable-filters-for-rotation","paper_url":"http://arxiv.org/abs/1711.07289v3","paper_title":"Learning Steerable Filters for Rotation Equivariant CNNs","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":11,"model":"VF-CNN (C12)","metrics":{"Dice":"0.808","Hausdorff Distance (mm)":"50.7"},"uses_additional_data":false,"paper_date":"2016-12-29","paper":"/paper/rotation-equivariant-vector-field-networks","paper_url":"http://arxiv.org/abs/1612.09346v3","paper_title":"Rotation equivariant vector field networks","code":"https://github.com/COGMAR/RotEqNet","n_code_links":3,"syntology":null},{"rank_in_archive_order":12,"model":"VF-CNN (C4)","metrics":{"Dice":"0.800","Hausdorff Distance (mm)":"49.9"},"uses_additional_data":false,"paper_date":"2016-12-29","paper":"/paper/rotation-equivariant-vector-field-networks","paper_url":"http://arxiv.org/abs/1612.09346v3","paper_title":"Rotation equivariant vector field networks","code":"https://github.com/COGMAR/RotEqNet","n_code_links":3,"syntology":null},{"rank_in_archive_order":13,"model":"Micro-Net (e)","metrics":{"Dice":"0.797","Hausdorff Distance (mm)":"51.9"},"uses_additional_data":false,"paper_date":"2018-04-22","paper":"/paper/micro-net-a-unified-model-for-segmentation-of","paper_url":"http://arxiv.org/abs/1804.08145v2","paper_title":"Micro-Net: A unified model for segmentation of various objects in microscopy images","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":14,"model":"FCN8 (e)","metrics":{"Dice":"0.797","Hausdorff Distance (mm)":"31.2"},"uses_additional_data":false,"paper_date":"2014-11-14","paper":"/paper/fully-convolutional-networks-for-semantic-1","paper_url":"http://arxiv.org/abs/1411.4038v2","paper_title":"Fully Convolutional Networks for Semantic Segmentation","code":"https://github.com/pochih/fcn-pytorch","n_code_links":51,"syntology":{"n_ran":3,"n_unverified":1,"n_samples":4,"n_pointer_only_licence":4}},{"rank_in_archive_order":15,"model":"G-CNN (C4)","metrics":{"Dice":"0.793","Hausdorff Distance (mm)":"49.0"},"uses_additional_data":false,"paper_date":"2016-02-24","paper":"/paper/group-equivariant-convolutional-networks","paper_url":"http://arxiv.org/abs/1602.07576v3","paper_title":"Group Equivariant Convolutional Networks","code":"https://github.com/adambielski/pytorch-gconv-experiments","n_code_links":1,"syntology":null},{"rank_in_archive_order":16,"model":"Steerable G-CNN (e)","metrics":{"Dice":"0.791","Hausdorff Distance (mm)":"51.0"},"uses_additional_data":false,"paper_date":"2017-11-20","paper":"/paper/learning-steerable-filters-for-rotation","paper_url":"http://arxiv.org/abs/1711.07289v3","paper_title":"Learning Steerable Filters for Rotation Equivariant CNNs","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":17,"model":"Mask R-CNN (e)","metrics":{"Dice":"0.760","Hausdorff Distance (mm)":"50.9"},"uses_additional_data":false,"paper_date":"2017-03-20","paper":"/paper/mask-r-cnn","paper_url":"http://arxiv.org/abs/1703.06870v3","paper_title":"Mask R-CNN","code":"https://github.com/tensorflow/models/tree/master/official/vision","n_code_links":179,"syntology":{"n_ran":42,"n_unverified":98,"n_samples":140,"n_pointer_only_licence":23}},{"rank_in_archive_order":18,"model":"U-Net (e)","metrics":{"Dice":"0.758","Hausdorff Distance (mm)":"47.8"},"uses_additional_data":false,"paper_date":"2015-05-18","paper":"/paper/u-net-convolutional-networks-for-biomedical","paper_url":"http://arxiv.org/abs/1505.04597v1","paper_title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":487,"syntology":{"n_ran":510,"n_unverified":247,"n_samples":757,"n_pointer_only_licence":426}}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":7,"rows_with_any_sample_ran":5,"distinct_papers_with_graph_line":6,"distinct_papers_with_any_sample_ran":5,"samples_over_distinct_papers":{"n_ran":557,"n_unverified":357,"n_samples":914,"n_pointer_only_licence":453,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":557,"n_unverified":363,"n_samples":920,"n_pointer_only_licence":453,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}