Skin Lesion Segmentation via Dense Connected Deconvolutional Network

Dermoscopy imaging analysis is a routine procedure for diagnosis and treatment of skin lesions. Segmentation is the very first step to demarcate skin lesions for further quantitative analysis. However, it is a challenging task due to various changes from different viewpoints and scales of skin lesions. To handle these challenges, we devise a new dense deconvolutional network (DDN) for skin lesion segmentation based on encoding module and decoding module. Our devised network consists of convolution unit, dense deconvolutionallayer (DDL) and chained residual pooling block. DDL is adopted to restore the high resolution of the original input by upsampling, while the chained residual pooling is utilized to fuse multilevel features. Also, the hierarchical supervision is added to capture low level detailed boundary information. The DDN is trained in an end-to-end manner and free of prior knowledge and complicated post-processing procedures. With fusing the local and global contextual information, the high-resolution prediction output is obtained. The validation on the public ISBI 2016 and 2017 skin lesion challenge dataset demonstrates the effectiveness of our proposed method.