| 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)
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| Department | 机器人学研究室 |
| Source Publication | ACM Transactions on Multimedia Computing, Communications and Applications
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| ISSN | 1551-6857 |
| 2017 | |
| Volume | 13Issue:2Pages:1-18 |
| Indexed By | SCI ; EI |
| EI Accession number | 20171503556213 |
| WOS ID | WOS:000401537300003 |
| Contribution Rank | 1 |
| Funding Organization | NSFC (61375014, 61533015, U1613214, 61333019, and 61401455) |
| Keyword | Computer-aided Diagnosis Multi-class Sparse Dictionary Learning Latent Concept Pooling Endoscopy |
| Abstract | Successful 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 Headings | Science & Technology ; Technology |
| WOS Subject | Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods |
| WOS Keyword | ABNORMAL EVENT DETECTION ; CAPSULE ENDOSCOPY ; IMAGE CLASSIFICATION ; FEATURE-SELECTION ; VIDEO SEGMENTATION ; RECOGNITION ; FEATURES ; DESCRIPTORS ; INFORMATION |
| WOS Research Area | Computer Science |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.sia.cn/handle/173321/20369 |
| Collection | 机器人学研究室 |
| Corresponding Author | Cong Y(丛杨) |
| Affiliation | 1.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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| Multi-class Latent C（1531KB） | 期刊论文 | 作者接受稿 | 开放获取 | ODC PDDL | View Download | |
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