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Exploring Web Images to Enhance Skin Disease Analysis Under A Computer Vision Framework
Xia, Yingjie1; Zhang, Luming2; Meng, Lei3; Yan, Yan4,5; Nie, Liqiang6; Li, Xuelong7
作者部门光学影像学习与分析中心
2018-11
发表期刊IEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267;2168-2275
卷号48期号:11页码:3080-3091
产权排序7
摘要

To benefit the skin care, this paper aims to design an automatic and effective visual analysis framework, with the expectation of recognizing the skin disease from a given image conveying the disease affected surface. This task is nontrivial, since it is hard to collect sufficient well-labeled samples. To address such problem, we present a novel transfer learning model, which is able to incorporate external knowledge obtained from the rich and relevant Web images contributed by grassroots. In particular, we first construct a target domain by crawling a small set of images from vertical and professional dermatological websites. We then construct a source domain by collecting a large set of skin disease related images from commercial search engines. To reinforce the learning performance in the target domain, we initially build a learning model in the target domain, and then seamlessly leverage the training samples in the source domain to enhance this learning model. The distribution gap between these two domains are bridged by a linear combination of Gaussian kernels. Instead of training models with low-level features, we resort to deep models to learn the succinct, invariant, and high-level image representations. Different from previous efforts that focus on a few types of skin diseases with a small and confidential set of images generated from hospitals, this paper targets at thousands of commonly seen skin diseases with publicly accessible Web images. Hence the proposed model is easily repeatable by other researchers and extendable to other disease types. Extensive experiments on a real-world dataset have demonstrated the superiority of our proposed method over the state-of-the-art competitors.

关键词Skin Disease Inference Transfer Learning
DOI10.1109/TCYB.2017.2765665
收录类别SCI
语种英语
WOS记录号WOS:000447825400005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/30682
专题光学影像学习与分析中心
通讯作者Zhang, Luming
作者单位1.Zhejiang Univ, Coll Comp Sci, Hangzhou 310027, Zhejiang, Peoples R China
2.Hefei Univ Technol, Dept Elect Engn & Informat Syst, Hefei 230009, Anhui, Peoples R China
3.Nanyang Technol Univ, Joint NTU UBC Res Ctr Excellence Act Living Elder, Singapore, Singapore
4.Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Trento, Italy
5.Adv Digital Sci Ctr, Singapore 138632, Singapore
6.Shandong Univ, Sch Comp Sci & Technol, Jinan, Shandong, Peoples R China
7.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714
Xia, Yingjie,Zhang, Luming,Meng, Lei,et al. Exploring Web Images to Enhance Skin Disease Analysis Under A Computer Vision Framework[J]. IEEE TRANSACTIONS ON CYBERNETICS,2018,48(11):3080-3091.
APA Xia, Yingjie,Zhang, Luming,Meng, Lei,Yan, Yan,Nie, Liqiang,&Li, Xuelong.(2018).Exploring Web Images to Enhance Skin Disease Analysis Under A Computer Vision Framework.IEEE TRANSACTIONS ON CYBERNETICS,48(11),3080-3091.
MLA Xia, Yingjie,et al."Exploring Web Images to Enhance Skin Disease Analysis Under A Computer Vision Framework".IEEE TRANSACTIONS ON CYBERNETICS 48.11(2018):3080-3091.
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