Dense-Residual Network with Adversarial Learning for Skin Lesion Segmentation

Abstract

Skin lesion segmentation in dermoscopic images is a challenging task in the domain of medical images analysis because of the irregular and blurring edges of the lesion and the presence of various artifacts. Inspired by the successful applications of the generative adversarial network (GAN), we propose a new neural network for the segmentation of skin lesion. Different from the traditional adversarial network, the segmentation network uses encoder-decoder with the dense-residual block which enables the network to be trained more efficiently. It can also establish a direct relationship between adjacent pixels to improve segmentation accuracy. A multi-scale objective loss function is used to utilize deep supervision in multiple layers of the critic network. End point error and Jaccard distance are combined as the content loss function. It can solve the problem of boundary ambiguity and solve the lesion-background imbalance in pixel-level classification for skin lesion segmentation. We finally use a joint loss function including a multi-scale objective loss function, end point error, and Jaccard distance content loss function. The experiment results show that our algorithm is superior to other state-of-the-art algorithms on the ISBI2017 and PH2 datasets.