PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones

By André G. C. Pacheco, G. R. Lima, A. Salomão, Breno Krohling, I. P. Biral, Gabriel G. de Angelo, Fábio C.R. Alves Jr, José G. M. Esgario, Alana C. Simora, Pedro B. C. Castro and 9 more. Data in Brief, 2020

359 citations recorded by the source corpus.

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.

Subfields: Dermatology, Epidemiology, Oncology

View Paper: https://www.semanticscholar.org/paper/6d7a6d578e9fa9b2e488ac98f14cc2bd610477ca

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Citations in the corpus

• Federated learning for medical image analysis: A survey (2023, neutral): “(2021) [123] Skin lesion classification HAM10000 [122] ISIC19 [124] Derm7pt [125] PAD-UFES [126] Dermoscopy CNN (EfficientNet)”
• Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset (2021, methodology): “With the exception of PAD-UFES-20 [42], none of the publicly available data sets identified by the Sixth ISIC Skin Image Analysis Workshop at CVPR 2021 (Derm7pt [30], Dermofit Image Library, ISIC 2018 [16, 52], ISIC 2019 [15, 52, 18], ISIC 2020[46, 17], MED-NODE [27], PH2 [37], SD-128 [49], SD-198, ”