Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 66, Issue 4, Pages 1006-1016Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2018.2866166
Keywords
Dermoscopy image; melanoma recognition; residual network; fisher vector; deep learning
Categories
Funding
- National Natural Science Foundation of China [81571758, 61871274, 61801305, 61501305, 81771922]
- National Key Research and Develop Program [2016YFC0104703]
- National Natural Science Foundation of Guangdong Province [2017A030313377, 2016A030313047]
- Shenzhen Peacock Plan [KQTD2016053112051497]
- Shenzhen Key Basic Research Project [JCYJ20170818142347251, JCYJ20170818094109846]
- Hong Kong RGC General Research Fund [PolyU152035/17E]
- National Taipei University of Technology-Shenzhen University Joint Research Program [2018006]
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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.
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