Dermoscopic Image Segmentation Through the Enhanced High-Level Parsing and Class Weighted Loss

Accurate skin lesion segmentation plays an important role in the computer aided analysis of melanoma. It is a challenging task due to the variation of skin lesion appearance, the low contrast with background, and the existence of the artifacts in dermoscopic images. In this paper, we try to boost the skin lesion segmentation performance based on the fully convolutional neural network. To this end, we first propose an enhanced high-level parsing (EHP) module to generate meaningful feature representation for skin lesion and make more precise delineation of the detailed lesion structure. Furthermore, to handle the imbalance data distribution of skin lesion and background, we propose a class weighted loss (CWL) to achieve more consistent lesion prediction. Experiment results evaluated on ISBI 2017 database demonstrate the effectiveness and robustness of the proposed architecture on skin lesion segmentation, achieving new state-of-the-art prediction performance.