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Multi-class Latent Concept Pooling for computer-aided endoscopy diagnosis
Wang S(王帅); Cong Y(丛杨); Fan HJ(范慧杰); Fan BJ(范保杰); Liu LQ(刘连庆); Yang YS(杨云生); Tang YD(唐延东); Zhao HC(赵怀慈); Yu HB(于海斌)
Department机器人学研究室
Source PublicationACM Transactions on Multimedia Computing, Communications and Applications
ISSN1551-6857
2017
Volume13Issue:2Pages:1-18
Indexed BySCI ; EI
EI Accession number20171503556213
WOS IDWOS:000401537300003
Contribution Rank1
Funding OrganizationNSFC (61375014, 61533015, U1613214, 61333019, and 61401455)
KeywordComputer-aided Diagnosis Multi-class Sparse Dictionary Learning Latent Concept Pooling Endoscopy
AbstractSuccessful computer-aided diagnosis systems typically rely on training datasets containing sufficient and richly annotated images. However, detailed image annotation is often time consuming and subjective, especially for medical images, which becomes the bottleneck for the collection of large datasets and then building computer-aided diagnosis systems. In this article, we design a novel computer-aided endoscopy diagnosis system to deal with the multi-classification problem of electronic endoscopy medical records (EEMRs) containing sets of frames, while labels of EEMRs can be mined from the corresponding text records using an automatic text-matching strategy without human special labeling. With unambiguous EEMR labels and ambiguous frame labels, we propose a simple but effective pooling scheme called Multi-class Latent Concept Pooling, which learns a codebook from EEMRs with different classes step by step and encodes EEMRs based on a soft weighting strategy. In our method, a computer-aided diagnosis system can be extended to new unseen classes with ease and applied to the standard single-instance classification problem even though detailed annotated images are unavailable. In order to validate our system, we collect 1,889 EEMRs with more than 59K frames and successfully mine labels for 348 of them. The experimental results show that our proposed system significantly outperforms the state-of-the-art methods. Moreover, we apply the learned latent concept codebook to detect the abnormalities in endoscopy images and compare it with a supervised learning classifier, and the evaluation shows that our codebook learning method can effectively extract the true prototypes related to different classes from the ambiguous data.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS KeywordABNORMAL EVENT DETECTION ; CAPSULE ENDOSCOPY ; IMAGE CLASSIFICATION ; FEATURE-SELECTION ; VIDEO SEGMENTATION ; RECOGNITION ; FEATURES ; DESCRIPTORS ; INFORMATION
WOS Research AreaComputer Science
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/20369
Collection机器人学研究室
Corresponding AuthorCong Y(丛杨)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
3.Chinese PLA General Hospital, Beijing, 100853, China
Recommended Citation
GB/T 7714
Wang S,Cong Y,Fan HJ,et al. Multi-class Latent Concept Pooling for computer-aided endoscopy diagnosis[J]. ACM Transactions on Multimedia Computing, Communications and Applications,2017,13(2):1-18.
APA Wang S.,Cong Y.,Fan HJ.,Fan BJ.,Liu LQ.,...&Yu HB.(2017).Multi-class Latent Concept Pooling for computer-aided endoscopy diagnosis.ACM Transactions on Multimedia Computing, Communications and Applications,13(2),1-18.
MLA Wang S,et al."Multi-class Latent Concept Pooling for computer-aided endoscopy diagnosis".ACM Transactions on Multimedia Computing, Communications and Applications 13.2(2017):1-18.
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