{"url":"/dataset/isic2016-task-1","name":"ISIC2016","full_name":"Lesion Segmentation","description_markdown":"Lesion segmentation data includes the original image, paired with the expert manual tracing of the lesion boundaries in the form of a binary mask. The Training Data file is a ZIP file, containing 900 dermoscopic lesion images in JPEG format. All images are named using the scheme ISIC_<image_id>.jpg, where <image_id> is a 7-digit unique identifier. EXIF tags in the images have been removed; any remaining EXIF tags should not be relied upon to provide accurate metadata. The Training Ground Truth file is a ZIP file, containing 900 binary mask images in PNG format. All masks are named using the scheme ISIC_<image_id>_Segmentation.png, where <image_id> matches the corresponding Training Data image for the mask. All mask images will have the exact same dimensions as their corresponding lesion image. Mask images are encoded as single-channel (grayscale) 8-bit PNGs (to provide lossless compression), where each pixel is either:\r\n\r\n0: representing the background of the image or areas outside the lesion\r\n255: representing the foreground of the image or areas inside the lesion","description_withheld":null,"homepage":"https://challenge.isic-archive.com/data/","introduced_date":"2016-05-04","introduced_date_note":null,"introduced_by":{"paper":"/paper/skin-lesion-analysis-toward-melanoma-2","title":"Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)","first_author":"David Gutman","url":null},"license":{"name":"https://creativecommons.org/share-your-work/public-domain/cc0/","url":"https://creativecommons.org/share-your-work/public-domain/cc0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Lesion Segmentation","url":"/task/lesion-segmentation","datasets_with_task":"/datasets/task/lesion-segmentation"},{"name":"Skin Lesion Segmentation","url":"/task/skin-lesion-segmentation","datasets_with_task":"/datasets/task/skin-lesion-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ISIC2016"],"data_loaders":[],"num_papers_in_archive":50,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/skin-lesion-segmentation-on-isic2016","task":"Skin Lesion Segmentation","dataset_variant":"ISIC2016","rows":1,"metrics":["ACC","Average IOU","Dice","MAE"],"first_row_in_archive_order":{"model":"QTSeg","paper":"/paper/qtseg-a-query-token-based-architecture-for","metrics":{"ACC":"96.41","Average IOU":"86.74","Dice":"92.42","MAE":"0.0359"},"code_links":[{"title":"tpnam0901/QTSeg","url":"https://github.com/tpnam0901/QTSeg"}]},"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}],"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."}