Skin lesion classification in dermoscopy images using synergic deep learning

Abstract

Automated skin lesion classification in the dermoscopy images is an essential way to improve diagnostic performance and reduce melanoma deaths. Although deep learning has shown proven advantages over traditional methods, which rely on handcrafted features, in image classification, it remains challenging to classify skin lesions due to the significant intra-class variation and inter-class similarity. In this paper, we propose a synergic deep learning (SDL) model to address this issue, which not only uses dual deep convolutional neural networks (DCNNs) but also enables them to mutually learn from each other. Specifically, we concatenate the image representation learned by both DCNNs as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images belong to the same class. We train the SDL model in the end-to-end manner under the supervision of the classification error in each DCNN and the synergic error. We evaluated our SDL model on the ISIC 2016 Skin Lesion Classification dataset and achieved the state-of-the-art performance.