{"url":"/dataset/glas","name":"GlaS","full_name":"Gland Segmentation in Colon Histology Images Challenge","description_markdown":"The dataset used in this challenge consists of 165 images derived from 16 H&E stained histological sections of stage T3 or T42 colorectal adenocarcinoma. Each section belongs to a different patient, and sections were processed in the laboratory on different occasions. Thus, the dataset exhibits high inter-subject variability in both stain distribution and tissue architecture. The digitization of these histological sections into whole-slide images (WSIs) was accomplished using a Zeiss MIRAX MIDI Slide Scanner with a pixel resolution of 0.465µm.\r\n\r\nSource: [Sirinukunwattana et al.](https://arxiv.org/pdf/1603.00275.pdf)\r\n\r\nImage source: [Sirinukunwattana et al.](https://arxiv.org/pdf/1603.00275.pdf)","description_withheld":null,"homepage":"https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/","introduced_date":"2016-03-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/gland-segmentation-in-colon-histology-images","title":"Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest","first_author":"Korsuk Sirinukunwattana","url":null},"license":{"name":"Custom (research-only, non-commercial, attribution)","url":"https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/download/"},"modalities":[{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["GlaS"],"data_loaders":[{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/glas-dataset","frameworks":["tf","pytorch"]},{"repo":"https://github.com/jeya-maria-jose/Medical-Transformer","url":"https://github.com/jeya-maria-jose/Medical-Transformer","frameworks":["pytorch"]}],"num_papers_in_archive":112,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-image-segmentation-on-glas","task":"Medical Image Segmentation","dataset_variant":"GlaS","rows":10,"metrics":["F1","IoU","Dice"],"first_row_in_archive_order":{"model":"Hi-gMISnet","paper":"/paper/hi-gmisnet-generalized-medical-image","metrics":{"Dice":"93.25","F1":"93.25"},"code_links":[{"title":"tushartalukder/HigMISnet","url":"https://github.com/tushartalukder/HigMISnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/trans2unet-neural-fusion-for-nuclei-semantic","title":"Trans2Unet: Neural fusion for Nuclei Semantic Segmentation","date":"2024-07-24","rows_on_this_dataset":1,"code_links":0,"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/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/uctransnet-rethinking-the-skip-connections-in","title":"UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer","date":"2021-09-09","rows_on_this_dataset":3,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+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/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}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+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."}