{"url":"/dataset/isic-2018-task-1","name":"ISIC 2018 Task 1","full_name":"ISIC 2018 Task 1","description_markdown":"The ISIC 2018 dataset was published by the International Skin Imaging Collaboration (ISIC) as a large-scale dataset of dermoscopy images. This Task 1 dataset is the challenge on lesion segmentation. It includes 2594 images.\r\n\r\nSource: [Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions](https://arxiv.org/abs/1909.00166)\r\nImage Source: [https://challenge2018.isic-archive.com/task1/](https://challenge2018.isic-archive.com/task1/)","description_withheld":null,"homepage":"https://challenge2018.isic-archive.com/task1/","introduced_date":"2019-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/skin-lesion-analysis-toward-melanoma-1","title":"Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)","first_author":"Noel Codella","url":null},"license":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":"Lesion Segmentation","url":"/task/lesion-segmentation","datasets_with_task":"/datasets/task/lesion-segmentation"},{"name":"Lesion Classification","url":"/task/lesion-classification","datasets_with_task":"/datasets/task/lesion-classification"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["ISIC 2018 Task 1"],"data_loaders":[{"repo":"https://github.com/SaoYan/GenerativeSkinLesion","url":"https://github.com/SaoYan/GenerativeSkinLesion","frameworks":["tf","pytorch"]}],"num_papers_in_archive":27,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/lesion-segmentation-on-isic-2018-task-1","task":"Lesion Segmentation","dataset_variant":"ISIC 2018 Task 1","rows":1,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"DCSAU-Net","paper":"/paper/dcsau-net-a-deeper-and-more-compact-split","metrics":{"mIoU":"0.8301"},"code_links":[{"title":"xq141839/DCSAU-Net","url":"https://github.com/xq141839/DCSAU-Net"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/dcsau-net-a-deeper-and-more-compact-split","title":"DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation","date":"2022-02-02","rows_on_this_dataset":1,"code_links":1,"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."}