{"url":"/dataset/medico-automatic-polyp-segmentation-challenge","name":"Medico automatic polyp segmentation challenge (dataset)","full_name":null,"description_markdown":"The “Medico automatic polyp segmentation challenge” aims to develop computer-aided diagnosis systems for automatic polyp segmentation to detect all types of polyps (for example, irregular polyp, smaller or flat polyps) with high efficiency and accuracy. The main goal of the challenge is to benchmark semantic segmentation algorithms on a publicly available dataset, emphasizing robustness, speed, and generalization.\r\n\r\n\r\nMedico Multimedia Task at MediaEval 2020:Automatic Polyp Segmentation (https://arxiv.org/pdf/2012.15244.pdf)","description_withheld":null,"homepage":"https://multimediaeval.github.io/editions/2020/tasks/medico/","introduced_date":"2020-12-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/medico-multimedia-task-at-mediaeval-2020","title":"Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation","first_author":"Debesh Jha","url":null},"license":{"name":"Open access","url":"https://multimediaeval.github.io/editions/2020/tasks/medico/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Biomedical","url":"/datasets/modality/biomedical"},{"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":"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":["Medico automatic polyp segmentation challenge (dataset)"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-image-segmentation-on-medico","task":"Medical Image Segmentation","dataset_variant":"Medico automatic polyp segmentation challenge (dataset)","rows":2,"metrics":["DSC","mIoU","Recall","Precision","FPS"],"first_row_in_archive_order":{"model":"NanoNet-A","paper":"/paper/nanonet-real-time-polyp-segmentation-in-video","metrics":{"DSC":"0.7364","FPS":"28.07","Precision":"0.7310","Recall":"0.8566","mIoU":"0.6319"},"code_links":[{"title":"DebeshJha/NanoNet","url":"https://github.com/DebeshJha/NanoNet"},{"title":"reshalfahsi/NanoNet-PyTorch","url":"https://github.com/reshalfahsi/NanoNet-PyTorch"},{"title":"reshalfahsi/NanoNet","url":"https://github.com/reshalfahsi/NanoNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/nanonet-real-time-polyp-segmentation-in-video","title":"NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy","date":"2021-04-22","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/automatic-polyp-segmentation-using-u-net","title":"Automatic Polyp Segmentation using U-Net-ResNet50","date":"2020-12-30","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+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."}