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

Understand

Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma.

• In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network.
• The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder.
• Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries.