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We acknowledge the support of the International Skin Imaging Collaboration (ISIC). This research was supported by the Spanish Research Agency (AEI) under project PID2020-116907RB-I00 of the call MCIN/ AEI /10.13039/501100011033 and the project 718/C/2019 with id 201923-30 and 201923-31, funded by Fundacio la Marato de TV3, iTOBOs grant from the European Union's Horizon 2020 research and innovation programme num 965221. Other funding sources include the Melanoma Research Alliance Young Investigator Award 614197. This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748.

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BCN20000: Dermoscopic Lesions in the Wild

Publicado en:Scientific Data. 11 (1): 641- - 2024-06-17 11(1), DOI: 10.1038/s41597-024-03387-w

Autores: Hernandez-Perez, Carlos; Combalia, Marc; Podlipnik, Sebastian; Codella, Noel C F; Rotemberg, Veronica; Halpern, Allan C; Reiter, Ofer; Carrera, Cristina; Barreiro, Alicia; Helba, Brian; Puig, Susana; Vilaplana, Veronica; Malvehy, Josep

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Advancements in dermatological artificial intelligence research require high-quality and comprehensive datasets that mirror real-world clinical scenarios. We introduce a collection of 18,946 dermoscopic images spanning from 2010 to 2016, collated at the Hospital Cl & iacute;nic in Barcelona, Spain. The BCN20000 dataset aims to address the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions in hard-to-diagnose locations such as those found in nails and mucosa, large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. Our dataset covers eight key diagnostic categories in dermoscopy, providing a diverse range of lesions for artificial intelligence model training. Furthermore, a ninth out-of-distribution (OOD) class is also present on the test set, comprised of lesions which could not be distinctively classified as any of the others. By providing a comprehensive collection of varied images, BCN20000 helps bridge the gap between the training data for machine learning models and the day-to-day practice of medical practitioners. Additionally, we present a set of baseline classifiers based on state-of-the-art neural networks, which can be extended by other researchers for further experimentation.

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