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Deep sparse feature selection for computer aided endoscopy diagnosis
Cong Y(丛杨); Wang S(王帅); Liu J(刘霁); Cao, Jun; Yang YS(杨云生); Luo JB(罗杰波)
Department机器人学研究室
Source PublicationPattern Recognition
ISSN0031-3203
2015
Volume48Issue:3Pages:907-917
Indexed BySCI ; EI
EI Accession number20144500169479
WOS IDWOS:000347747000024
Contribution Rank1
KeywordEndoscopy Feature Extraction
AbstractIn this paper, we develop a computer aided diagnosis algorithm to detect and classify the abnormalities in vision-based endoscopic examination. We focus on analyzing the traditional gastroscope data and help the medical experts improve the accuracy of medical diagnosis with our analysis tool. To achieve this, we first segment the image into superpixels, then extract various color and texture features from them and combine the features into one feature vector to represent the images. This approach is more flexible and accurate than the traditional patch-based image representation. Then we design a novel feature selection model with group sparsity, Deep Sparse SVM (DSSVM) that not only can assign a suitable weight to the feature dimensions like the other traditional feature selection models, but also directly exclude useless features from the feature pool. Thus, our DSSVM model can maintain the accuracy while reducing the computation complexity. Moreover, the image quality is also pre-assessed. For the experiments, we build a new gastroscope dataset with a total of about 3800 images from 1284 volunteers, and conducted various experiments and comparisons with other algorithms to justify the effectiveness and efficiency of our algorithm.
Language英语
Citation statistics
Cited Times:24[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/15278
Collection机器人学研究室
Corresponding AuthorCong Y(丛杨)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China
2.Department of Computer Science, University of Rochester, United States
3.Department of Computer Science, Arizona State University, United States
4.Chinese PLA General Hospital, China
5.University of Chinese Academy of Sciences, China
Recommended Citation
GB/T 7714
Cong Y,Wang S,Liu J,et al. Deep sparse feature selection for computer aided endoscopy diagnosis[J]. Pattern Recognition,2015,48(3):907-917.
APA Cong Y,Wang S,Liu J,Cao, Jun,Yang YS,&Luo JB.(2015).Deep sparse feature selection for computer aided endoscopy diagnosis.Pattern Recognition,48(3),907-917.
MLA Cong Y,et al."Deep sparse feature selection for computer aided endoscopy diagnosis".Pattern Recognition 48.3(2015):907-917.
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