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 | |
刊名 | IEEE TRANSACTIONS ON CYBERNETICS |
2018-11 | |
卷号 | 48期号:11页码:3080-3091 |
关键词 | Skin Disease Inference Transfer Learning |
ISSN号 | 2168-2267;2168-2275 |
DOI | 10.1109/TCYB.2017.2765665 |
产权排序 | 7 |
英文摘要 | 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. |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000447825400005 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/30682] |
专题 | 西安光学精密机械研究所_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. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论