Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists' experience and fails to achieve adequate accuracy and reproducibility in discriminating atypical nevi (AN) from early melanomas (EM).
A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi / Tognetti, Linda; Bonechi, Simone; Andreini, Paolo; Bianchini, Monica; Scarselli, Franco; Cevenini, Gabriele; Moscarella, Elvira; Farnetani, Francesca; Longo, Caterina; Lallas, Aimilios; Carrera, Cristina; Puig, Susana; Tiodorovic, Danica; Perrot, Jean Luc; Pellacani, Giovanni; Argenziano, Giuseppe; Cinotti, Elisa; Cataldo, Gennaro; Balistreri, Alberto; Mecocci, Alessandro; Gori, Marco; Rubegni, Pietro; Cartocci, Alessandra. - In: JOURNAL OF DERMATOLOGICAL SCIENCE. - ISSN 0923-1811. - 101:2(2021), pp. 115-122. [10.1016/j.jdermsci.2020.11.009]
A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi
Farnetani, Francesca;Longo, Caterina;
2021
Abstract
Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists' experience and fails to achieve adequate accuracy and reproducibility in discriminating atypical nevi (AN) from early melanomas (EM).
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