Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features

2018/08/20 by Zhen Yu, Xudong Jiang, Feng Zhou +5 · 30 citations
Medicine · Computer Science · Biochemistry, Genetics and Molecular Biology · #Cutaneous Melanoma Detection and Management #AI in cancer detection #melanin and skin pigmentation

paper · doi:10.1109/tbme.2018.2866166

Abstract

In this paper, we present a novel framework for dermoscopy image recognition via both a deep learning method and a local descriptor encoding strategy. Specifically, deep representations of a rescaled dermoscopy image are first extracted via a very deep residual neural network pretrained on a large natural image dataset. Then these local deep descriptors are aggregated by orderless visual statistic features based on Fisher vector (FV) encoding to build a global image representation. Finally, the FV encoded representations are used to classify melanoma images using a support vector machine with a Chi-squared kernel. Our proposed method is capable of generating more discriminative features to deal with large variations within melanoma classes, as well as small variations between melanoma and nonmelanoma classes with limited training data. Extensive experiments are performed to demonstrate the effectiveness of our proposed method. Comparisons with state-of-the-art methods show the superiority of our method using the publicly available ISBI 2016 Skin lesion challenge dataset.

Citations

• ImageNet classification with deep convolutional neural networks

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