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In this study, a multi-task deep neural network is proposed for skin lesionanalysis. The proposed multi-task learning model solves different tasks (e.g.,lesion segmentation and two independent binary lesion classifications) at thesame time by exploiting commonalities and differences across tasks. Thisresults in improved learning efficiency and potential prediction accuracy forthe task-specific models, when compared to training the individual modelsseparately. The proposed multi-task deep learning model is trained andevaluated on the dermoscopic image sets from the International Skin ImagingCollaboration (ISIC) 2017 Challenge - Skin Lesion Analysis towards MelanomaDetection, which consists of 2000 training samples and 150 evaluation samples.The experimental results show that the proposed multi-task deep learning modelachieves promising performances on skin lesion segmentation and classification.The average value of Jaccard index for lesion segmentation is 0.724, while theaverage values of area under the receiver operating characteristic curve (AUC)on two individual lesion classifications are 0.880 and 0.972, respectively.

A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification