Segmentation of Lesion in Dermoscopy Images Using Dense-Residual Network with Adversarial Learning

In the field of medical images, skin lesion segmentation in dermoscopic images is a challenging task due to the irregular and blurring edges of the lesion and the presence of various artifacts. With the successful application of generative antagonistic network (GAN), a new neural network for skin lesion segmentation is proposed. The encoder-decoder with Dense-Residual block is used in the segmentation network which enables the network to be trained more efficiently. A multi-scale objective loss function is introduced to utilize deep supervision. We combine Jaccard distance and End Point Error which can solve lesion-background imbalance problem in pixel-level classification for skin lesion segmentation and also alleviate the problem of boundary ambiguity. A joint loss function is finally used, which includes a multi-scale objective loss function, End Point Error and Jaccard distance content loss function. Experiment results show that our algorithm is superior to other state-of-the-art algorithms on the ISBI2017.