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