Deep learning for skin lesion segmentation

Abstract

Melanomas are the most aggressive form of skin cancer. Due to observer bias, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion areas in the dermoscopy images. In this paper, we present a deep learning method for automatic skin lesion segmentation. We use a subset of the International Skin Imaging Collaboration (ISIC) Archive dataset, which contains dermoscopic images paired with their corresponding lesion binary masks, provided by IEEE International Symposium on Biomedical Imaging (ISBI) 2017 challenge for Skin Lesion Analysis Towards Melanoma Detection, and compare against the benchmark results submitted by other participants. The experimental results show that our proposed method can outperform the submissions in terms of segmentation accuracy.

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