Improving Skin Lesion Segmentation with Deep Convolutional Generative Adversarial Networks

Abstract

Accurate segmentation of skin lesion is an important step in computer aided diagnosis of skin cancer. Recently, deep-learning-based image segmentation methods have drawn much attention and shown exacting results. Unfortunately, existing skin disease datasets can hardly satisfy the requirement of massive training samples for deep neural networks. To meet this challenge, we introduce a data augmentation method using deep convolutional generative adversarial network (DCGAN), which can generate realistic samples with lesion features learned from the existing dataset, increasing both the quantity and diversity of training samples. The architecture of our proposed segmentation algorithm is built upon deep fully connected networks (FCN) and the DenseNet is employed as feature extractor. Extensive experiments are carried out on “ISBI 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge dataset”, and the results demonstrate that the proposed algorithm significantly improves the accuracy of skin lesions segmentation without requiring new actual training samples.