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Skin lesion segmentation (SLS) in dermoscopic images is a crucial task forautomated diagnosis of melanoma. In this paper, we present a robust deeplearning SLS model, so-called SLSDeep, which is represented as anencoder-decoder network. The encoder network is constructed by dilated residuallayers, in turn, a pyramid pooling network followed by three convolution layersis used for the decoder. Unlike the traditional methods employing across-entropy loss, we investigated a loss function by combining both NegativeLog Likelihood (NLL) and End Point Error (EPE) to accurately segment themelanoma regions with sharp boundaries. The robustness of the proposed modelwas evaluated on two public databases: ISBI 2016 and 2017 for skin lesionanalysis towards melanoma detection challenge. The proposed model outperformsthe state-of-the-art methods in terms of segmentation accuracy. Moreover, it iscapable to segment more than $100$ images of size 384x384 per second on arecent GPU.

SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks