{"url":"/dataset/isic-2019","name":"ISIC 2019","full_name":null,"description_markdown":"The goal for ISIC 2019 is classify dermoscopic images among nine different diagnostic categories.25,331 images are available for training across 8 different categories. Two tasks will be available for participation: 1) classify dermoscopic images without meta-data,\r\nand 2) classify images with additional available meta-data.","description_withheld":null,"homepage":"https://challenge.isic-archive.com/landing/2019/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"},{"name":"Partial Label Learning","url":"/task/partial-label-learning","datasets_with_task":"/datasets/task/partial-label-learning"},{"name":"Skin Lesion Classification","url":"/task/skin-lesion-classification","datasets_with_task":"/datasets/task/skin-lesion-classification"}],"languages":[],"variants":["ISIC 2019"],"data_loaders":[],"num_papers_in_archive":13,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/skin-lesion-classification-on-isic-2019","task":"Skin Lesion Classification","dataset_variant":"ISIC 2019","rows":2,"metrics":["Accuracy","Balanced Accuracy"],"first_row_in_archive_order":{"model":"Ensemble","paper":"/paper/analysis-of-skin-lesion-images-with-deep","metrics":{"Accuracy":"0.634"},"code_links":[{"title":"j05t/lesion-analysis","url":"https://github.com/j05t/lesion-analysis"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/classification-on-isic-2019","task":"Classification","dataset_variant":"ISIC 2019","rows":1,"metrics":["Balanced Multi-Class Accuracy"],"first_row_in_archive_order":{"model":"CASS","paper":"/paper/cass-cross-architectural-self-supervision-for","metrics":{"Balanced Multi-Class Accuracy":"0.6519"},"code_links":[{"title":"pranavsinghps1/CASS","url":"https://github.com/pranavsinghps1/CASS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/partial-label-learning-on-isic-2019","task":"Partial Label Learning","dataset_variant":"ISIC 2019","rows":1,"metrics":["Balanced Multi-Class Accuracy"],"first_row_in_archive_order":{"model":"CASS","paper":"/paper/cass-cross-architectural-self-supervision-for","metrics":{"Balanced Multi-Class Accuracy":"0.7258"},"code_links":[{"title":"pranavsinghps1/CASS","url":"https://github.com/pranavsinghps1/CASS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/liwterm-a-lightweight-transformer-based-model","title":"LiwTERM: A Lightweight Transformer-Based Model for Dermatological Multimodal Lesion Detection","date":"2024-10-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cass-cross-architectural-self-supervision-for","title":"CASS: Cross Architectural Self-Supervision for Medical Image Analysis","date":"2022-06-08","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/analysis-of-skin-lesion-images-with-deep","title":"Analysis of skin lesion images with deep learning","date":"2021-01-11","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."}