{"url":"/dataset/monuseg","name":"MoNuSeg","full_name":null,"description_markdown":"The dataset for this challenge was obtained by carefully annotating tissue images of several patients with tumors of different organs and who were diagnosed at multiple hospitals. This dataset was created by downloading H&E stained tissue images captured at 40x magnification from TCGA archive. H&E staining is a routine protocol to enhance the contrast of a tissue section and is commonly used for tumor assessment (grading, staging, etc.). Given the diversity of nuclei appearances across multiple organs and patients, and the richness of staining protocols adopted at multiple hospitals, the training datatset will enable the development of robust and generalizable nuclei segmentation techniques that will work right out of the box.\r\n\r\nSource: [MoNuSeg](https://monuseg.grand-challenge.org/Data/)","description_withheld":null,"homepage":"https://monuseg.grand-challenge.org/Data/","introduced_date":null,"introduced_date_note":null,"introduced_by":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":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"},{"name":"Cell Segmentation","url":"/task/cell-segmentation","datasets_with_task":"/datasets/task/cell-segmentation"},{"name":"Unsupervised nucleus segmentation","url":"/task/unsupervised-nucleus-segmentation","datasets_with_task":"/datasets/task/unsupervised-nucleus-segmentation"}],"languages":[],"variants":["MoNuSeg"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/RationAI/MoNuSeg","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":17,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-image-segmentation-on-monuseg","task":"Medical Image Segmentation","dataset_variant":"MoNuSeg","rows":15,"metrics":["F1","IoU","AHD95","ASD","mIoU"],"first_row_in_archive_order":{"model":"Stardist","paper":"/paper/cell-detection-with-star-convex-polygons","metrics":{"F1":"84.6"},"code_links":[{"title":"stardist/stardist","url":"https://github.com/stardist/stardist"},{"title":"uhlmanngroup/splinedist","url":"https://github.com/uhlmanngroup/splinedist"},{"title":"hthierno/pytorch-stardist","url":"https://github.com/hthierno/pytorch-stardist"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/cell-segmentation-on-monuseg-1","task":"Cell Segmentation","dataset_variant":"MoNuSeg","rows":1,"metrics":["Average Dice"],"first_row_in_archive_order":{"model":"PromptNu","paper":"/paper/prompting-vision-language-model-for-nuclei","metrics":{"Average Dice":"0.838"},"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"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rethinking-decoder-design-improving-biomarker-1","title":"Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention","date":"2025-06-23","rows_on_this_dataset":1,"code_links":1,"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/rethinking-the-nested-u-net-approach","title":"Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusion","date":"2025-04-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/hi-gmisnet-generalized-medical-image","title":"Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN","date":"2024-05-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/masked-diffusion-as-self-supervised","title":"Masked Diffusion as Self-supervised Representation Learner","date":"2023-08-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dual-cross-attention-for-medical-image","title":"Dual Cross-Attention for Medical Image Segmentation","date":"2023-03-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/histoseg-quick-attention-with-multi-loss","title":"HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images","date":"2022-09-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lvit-language-meets-vision-transformer-in","title":"LViT: Language meets Vision Transformer in Medical Image Segmentation","date":"2022-06-29","rows_on_this_dataset":5,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+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/medical-transformer-gated-axial-attention-for","title":"Medical Transformer: Gated Axial-Attention for Medical Image Segmentation","date":"2021-02-21","rows_on_this_dataset":3,"code_links":2,"syntology":null},{"paper":"/paper/cell-detection-with-star-convex-polygons","title":"Cell Detection with Star-convex Polygons","date":"2018-06-09","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":33,"samples_ran":5,"samples_unverified":28,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":3,"samples_harvested":35,"samples_ran":7,"samples_unverified":28,"pointer_only_for_licence":3,"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."}