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|Title:||Melanoma recognition in dermoscopy images via aggregated deep convolutional features||Authors:||Yu, Zhen
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2019||Source:||Yu, Z., Jiang, X., Zhou, F., Qin, J., Ni, D., Chen, S., ... Wang, T. (2019). Melanoma recognition in dermoscopy images via aggregated deep convolutional features. IEEE Transactions on Biomedical Engineering, 66(4), 1006-1016. doi:10.1109/TBME.2018.2866166||Journal:||IEEE Transactions on Biomedical Engineering||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.||URI:||https://hdl.handle.net/10356/145335||ISSN:||1558-2531||DOI:||10.1109/TBME.2018.2866166||Rights:||© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TBME.2018.2866166||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
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