{"url":"/dataset/bkai-igh-neopolyp-small","name":"BKAI-IGH NeoPolyp-Small","full_name":null,"description_markdown":"This dataset contains 1200 images (1000 WLI images and 200 FICE images) with fine-grained segmentation annotations. The training set consists of 1000 images, and the test set consists of 200 images. All polyps are classified into neoplastic or non-neoplastic classes denoted by red and green colors, respectively.  This dataset is a part of a bigger dataset called NeoPolyp.","description_withheld":null,"homepage":"https://bkai.ai/research/bkai-igh-neopolyp-small-a-dataset-for-fine-grained-polyp-segmentation/?fbclid=IwAR0x-wOB-74KOPW9G2jNtSCcfM4ybiLNZZuPTbDCCxE4MZHRCXR7-GoAo94","introduced_date":"2021-07-11","introduced_date_note":null,"introduced_by":{"paper":"/paper/neounet-towards-accurate-colon-polyp","title":"NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection","first_author":"Phan Ngoc Lan","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"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":[],"variants":["BKAI-IGH NeoPolyp-Small"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-image-segmentation-on-bkai-igh","task":"Medical Image Segmentation","dataset_variant":"BKAI-IGH NeoPolyp-Small","rows":9,"metrics":["Average Dice","mIoU","Average Dice (5-folds)","MAE (5-folds)","mIoU (5-folds)"],"first_row_in_archive_order":{"model":"RaBiT","paper":"/paper/rabit-an-efficient-transformer-using","metrics":{"Average Dice":"0.94","mIoU":"0.886"},"code_links":[{"title":"nguyenhoangthuan99/RaBiT","url":"https://github.com/nguyenhoangthuan99/RaBiT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/qtseg-a-query-token-based-architecture-for","title":"QTSeg: A Query Token-Based Architecture for Efficient 2D Medical Image Segmentation","date":"2024-12-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/emcad-efficient-multi-scale-convolutional","title":"EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation","date":"2024-05-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":23,"samples_ran":20,"samples_unverified":3,"pointer_only_for_licence":23,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/rabit-an-efficient-transformer-using","title":"RaBiT: An Efficient Transformer using Bidirectional Feature Pyramid Network with Reverse Attention for Colon Polyp Segmentation","date":"2023-07-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/focal-unet-unet-like-focal-modulation-for","title":"Focal-UNet: UNet-like Focal Modulation for Medical Image Segmentation","date":"2022-12-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/transresu-net-transformer-based-resu-net-for","title":"TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation","date":"2022-06-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/tganet-text-guided-attention-for-improved","title":"TGANet: Text-guided attention for improved polyp segmentation","date":"2022-05-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/blazeneo-blazing-fast-polyp-segmentation-and","title":"BlazeNeo: Blazing fast polyp segmentation and neoplasm detection","date":"2022-02-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/neounet-towards-accurate-colon-polyp","title":"NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection","date":"2021-07-11","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/real-time-polyp-detection-localisation-and","title":"Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning","date":"2020-11-15","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":23,"samples_ran":20,"samples_unverified":3,"pointer_only_for_licence":23,"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."}