Deep Bayesian Hashing With Center Prior for Multi-Modal Neuroimage Retrieval
文献类型:期刊论文
作者 | Yang, Erkun1,2; Liu, Mingxia1,2; Yao, Dongren1,2,3,4; Cao, Bing1,2,5; Lian, Chunfeng1,2; Yap, Pew-Thian1,2; Shen, Dinggang1,2,6 |
刊名 | IEEE TRANSACTIONS ON MEDICAL IMAGING |
出版日期 | 2021-02-01 |
卷号 | 40期号:2页码:503-513 |
ISSN号 | 0278-0062 |
关键词 | Semantics Bayes methods Hash functions Image retrieval Visualization Correlation Deep Bayesian hashing retrieval multi-modal neuroimage MRI PET |
DOI | 10.1109/TMI.2020.3030752 |
英文摘要 | Multi-modal neuroimage retrieval has greatly facilitated the efficiency and accuracy of decision making in clinical practice by providing physicians with previous cases (with visually similar neuroimages) and corresponding treatment records. However, existing methods for image retrieval usually fail when applied directly to multi-modal neuroimage databases, since neuroimages generally have smaller inter-class variation and larger inter-modal discrepancy compared to natural images. To this end, we propose a deep Bayesian hash learning framework, called CenterHash, which can map multi-modal data into a shared Hamming space and learn discriminative hash codes from imbalanced multi-modal neuroimages. The key idea to tackle the small inter-class variation and large inter-modal discrepancy is to learn a common center representation for similar neuroimages from different modalities and encourage hash codes to be explicitly close to their corresponding center representations. Specifically, we measure the similarity between hash codes and their corresponding center representations and treat it as a center prior in the proposed Bayesian learning framework. A weighted contrastive likelihood loss function is also developed to facilitate hash learning from imbalanced neuroimage pairs. Comprehensive empirical evidence shows that our method can generate effective hash codes and yield state-of-the-art performance in cross-modal retrieval on three multi-modal neuroimage datasets. |
资助项目 | NIH[AG041721] ; NIH[AG053867] |
WOS研究方向 | Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000615044900005 |
资助机构 | NIH |
源URL | [http://ir.ia.ac.cn/handle/173211/43225] |
专题 | 自动化研究所_脑网络组研究中心 |
通讯作者 | Liu, Mingxia; Shen, Dinggang |
作者单位 | 1.Univ North Carolina Chapel Hill, BRIC, Chapel Hill, NC 27599 USA 2.Univ North Carolina Chapel Hill, Dept Radiol, Chapel Hill, NC 27599 USA 3.Univ Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 5.Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China 6.Korea Univ, Dept Artificial Intelligence, Seoul 02841, South Korea |
推荐引用方式 GB/T 7714 | Yang, Erkun,Liu, Mingxia,Yao, Dongren,et al. Deep Bayesian Hashing With Center Prior for Multi-Modal Neuroimage Retrieval[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2021,40(2):503-513. |
APA | Yang, Erkun.,Liu, Mingxia.,Yao, Dongren.,Cao, Bing.,Lian, Chunfeng.,...&Shen, Dinggang.(2021).Deep Bayesian Hashing With Center Prior for Multi-Modal Neuroimage Retrieval.IEEE TRANSACTIONS ON MEDICAL IMAGING,40(2),503-513. |
MLA | Yang, Erkun,et al."Deep Bayesian Hashing With Center Prior for Multi-Modal Neuroimage Retrieval".IEEE TRANSACTIONS ON MEDICAL IMAGING 40.2(2021):503-513. |
入库方式: OAI收割
来源:自动化研究所
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