{"url":"/dataset/endotect-polyp-segmentation","name":"Endotect Polyp Segmentation Challenge Dataset","full_name":null,"description_markdown":"A challenge that consists of three tasks, each targeting a different requirement for in-clinic use. The first task involves classifying images from the GI tract into 23 distinct classes. The second task focuses on efficiant classification measured by the amount of time spent processing each image. The last task relates to automatcially segmenting polyps.\r\n\r\nSource: [EndoTect Challenge](https://endotect.com/)\r\n\r\nPlease cite \"The EndoTect 2020 Challenge: Evaluation andComparison of Classification, Segmentation and Inference Time for Endoscopy\" if you use the dataset.","description_withheld":null,"homepage":"https://endotect.com/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":{"name":"Open-access","url":"https://endotect.com/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Biomedical","url":"/datasets/modality/biomedical"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"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":"Real-Time Object Detection","url":"/task/real-time-object-detection","datasets_with_task":"/datasets/task/real-time-object-detection"},{"name":"Real-Time Semantic Segmentation","url":"/task/real-time-semantic-segmentation","datasets_with_task":"/datasets/task/real-time-semantic-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Endotect Polyp Segmentation Challenge Dataset"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-image-segmentation-on-endotect-polyp","task":"Medical Image Segmentation","dataset_variant":"Endotect Polyp Segmentation Challenge Dataset","rows":2,"metrics":["DSC","mIoU","FPS"],"first_row_in_archive_order":{"model":"DDANet","paper":"/paper/ddanet-dual-decoder-attention-network-for","metrics":{"DSC":"0.7870","FPS":"70.23","mIoU":"0.701"},"code_links":[{"title":"nikhilroxtomar/DDANet","url":"https://github.com/nikhilroxtomar/DDANet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/multi-kernel-positional-embedding-convnext","title":"Multi Kernel Positional Embedding ConvNeXt for Polyp Segmentation","date":"2023-01-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ddanet-dual-decoder-attention-network-for","title":"DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation","date":"2020-12-30","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}