{"url":"/dataset/pannuke","name":"PanNuke","full_name":null,"description_markdown":"PanNuke is a semi automatically generated nuclei instance segmentation and classification dataset with exhaustive nuclei labels across 19 different tissue types. The dataset consists of 481 visual fields, of which 312 are randomly sampled from more than 20K whole slide images at different magnifications, from multiple data sources. In total the dataset contains 205,343 labeled nuclei, each with an instance segmentation mask. \r\n\r\nSource: [PanNuke Dataset Extension, Insights and Baselines](/paper/pannuke-dataset-extension-insights-and)\r\nImage Source: [https://jgamper.github.io/PanNukeDataset/](https://jgamper.github.io/PanNukeDataset/)","description_withheld":null,"homepage":"https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/pannuke-dataset-extension-insights-and","title":"PanNuke Dataset Extension, Insights and Baselines","first_author":"Jevgenij Gamper","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Panoptic Segmentation","url":"/task/panoptic-segmentation","datasets_with_task":"/datasets/task/panoptic-segmentation"},{"name":"Cell Segmentation","url":"/task/cell-segmentation","datasets_with_task":"/datasets/task/cell-segmentation"},{"name":"Cell Detection","url":"/task/cell-detection","datasets_with_task":"/datasets/task/cell-detection"},{"name":"Multi-tissue Nucleus Segmentation","url":"/task/multi-tissue-nucleus-segmentation","datasets_with_task":"/datasets/task/multi-tissue-nucleus-segmentation"},{"name":"whole slide images","url":"/task/whole-slide-images","datasets_with_task":"/datasets/task/whole-slide-images"},{"name":"Selection bias","url":"/task/selection-bias","datasets_with_task":"/datasets/task/selection-bias"}],"languages":[],"variants":["PanNuke"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/RationAI/PanNuke","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/Mr-TalhaIlyas/Prerpcessing-PanNuke-Nuclei-Instance-Segmentation-Dataset","url":"https://github.com/Mr-TalhaIlyas/Prerpcessing-PanNuke-Nuclei-Instance-Segmentation-Dataset","frameworks":["tf"]}],"num_papers_in_archive":61,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/panoptic-segmentation-on-pannuke","task":"Panoptic Segmentation","dataset_variant":"PanNuke","rows":4,"metrics":["PQ"],"first_row_in_archive_order":{"model":"LKCell","paper":"/paper/lkcell-efficient-cell-nuclei-instance","metrics":{"PQ":"50.80"},"code_links":[{"title":"hustvl/lkcell","url":"https://github.com/hustvl/lkcell"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/cell-detection-on-pannuke","task":"Cell Detection","dataset_variant":"PanNuke","rows":1,"metrics":["Average F1","Average Precision","Average Recall"],"first_row_in_archive_order":{"model":"CellViT-SAM-H","paper":"/paper/cellvit-vision-transformers-for-precise-cell","metrics":{"Average F1":"0.83","Average Precision":"0.84","Average Recall":"0.81"},"code_links":[{"title":"tio-ikim/cellvit","url":"https://github.com/tio-ikim/cellvit"},{"title":"philippendres/CellPilot","url":"https://github.com/philippendres/CellPilot"},{"title":"junlinguo/cellvit-kidney","url":"https://github.com/junlinguo/cellvit-kidney"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/cell-segmentation-on-pannuke","task":"Cell Segmentation","dataset_variant":"PanNuke","rows":1,"metrics":["Average Dice"],"first_row_in_archive_order":{"model":"PromptNu","paper":"/paper/prompting-vision-language-model-for-nuclei","metrics":{"Average Dice":"0.860"},"code_links":[{"title":"NucleiDet/PromptNu","url":"https://github.com/NucleiDet/PromptNu"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multi-tissue-nucleus-segmentation-on-pannuke","task":"Multi-tissue Nucleus Segmentation","dataset_variant":"PanNuke","rows":1,"metrics":["Dice","Jaccard Index","PQ"],"first_row_in_archive_order":{"model":"SONNET","paper":"/paper/sonnet-a-self-guided-ordinal-regression","metrics":{"Dice":"0.824","Jaccard Index":"0.686","PQ":"0.649"},"code_links":[{"title":"QuIIL/Sonnet","url":"https://github.com/QuIIL/Sonnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/prompting-vision-language-model-for-nuclei","title":"Prompting Vision-Language Model for Nuclei Instance Segmentation and Classification","date":"2025-03-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/nulite-lightweight-and-fast-model-for-nuclei","title":"NuLite -- Lightweight and Fast Model for Nuclei Instance Segmentation and Classification","date":"2024-08-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lkcell-efficient-cell-nuclei-instance","title":"LKCell: Efficient Cell Nuclei Instance Segmentation with Large Convolution Kernels","date":"2024-07-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cellvit-vision-transformers-for-precise-cell","title":"CellViT: Vision Transformers for Precise Cell Segmentation and Classification","date":"2023-06-27","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/tsfd-net-tissue-specific-feature-distillation","title":"TSFD-Net: Tissue specific feature distillation network for nuclei segmentation and classification","date":"2022-03-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sonnet-a-self-guided-ordinal-regression","title":"SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images","date":"2022-02-09","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":1,"samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"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."}