{"url":"/dataset/pad-ufes-20","name":"PAD-UFES-20","full_name":null,"description_markdown":"Over the past few years, different Computer-Aided Diagnosis (CAD) systems have been proposed to tackle skin lesion analysis. Most of these systems work only for dermoscopy images since there is a strong lack of public clinical images archive available to evaluate the aforementioned CAD systems. To fill this gap, we release a skin lesion benchmark composed of clinical images collected from smartphone devices and a set of patient clinical data containing up to 21 features. The dataset consists of 1373 patients, 1641 skin lesions, and 2298 images for six different diagnostics: three skin diseases and three skin cancers. In total, 58.4% of the skin lesions are biopsy-proven, including 100% of the skin cancers. By releasing this benchmark, we aim to support future research and the development of new tools to assist clinicians to detect skin cancer.","description_withheld":null,"homepage":"https://data.mendeley.com/datasets/zr7vgbcyr2/1","introduced_date":"2020-07-07","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Skin Lesion Classification","url":"/task/skin-lesion-classification","datasets_with_task":"/datasets/task/skin-lesion-classification"}],"languages":[],"variants":["PAD-UFES-20"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/skin-lesion-classification-on-pad-ufes-20","task":"Skin Lesion Classification","dataset_variant":"PAD-UFES-20","rows":3,"metrics":["Balanced Accuracy"],"first_row_in_archive_order":{"model":"LiwTERM model","paper":"/paper/liwterm-a-lightweight-transformer-based-model","metrics":{"Balanced Accuracy":"0.74"},"code_links":[{"title":"luisouza/liwterm","url":"https://github.com/luisouza/liwterm"}]},"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/a-deep-learning-based-multimodal-fusion-model","title":"A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata","date":"2022-10-04","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/an-attention-based-mechanism-to-combine","title":"An Attention-Based Mechanism to Combine Images and Metadata in Deep Learning Models Applied to Skin Cancer Classification","date":"2021-02-26","rows_on_this_dataset":1,"code_links":0,"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."}