Multi-class Latent Concept Pooling for computer-aided endoscopy diagnosis
文献类型：期刊论文
| 作者 | 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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| 刊名 | ACM Transactions on Multimedia Computing, Communications and Applications
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| 出版日期 | 2017 |
| 卷号 | 13期号:2页码:1-18 |
| 关键词 | Computer-aided diagnosis multi-class sparse dictionary learning latent concept pooling endoscopy |
| ISSN号 | 1551-6857 |
| 产权排序 | 1 |
| 通讯作者 | 丛杨 |
| 中文摘要 | 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. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.sia.cn/handle/173321/20369]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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