CASIA OpenIR
Spatial non-local attention for thoracic disease diagnosis and visualisation in weakly supervised learning
Yang, Menglin1,2; Li, Ding1; Zhang, Wensheng1,2
Source PublicationIET IMAGE PROCESSING
ISSN1751-9659
2019-09-19
Volume13Issue:11Pages:1922-1930
Corresponding AuthorZhang, Wensheng(zhangwenshengia@hotmail.com)
AbstractWeakly supervised learning is capable of achieving fine-grained tasks with coarse annotations, which has shown great potential in computer-aided diagnosis. This study aims to achieve thoracic disease diagnosis in a weakly supervised manner only with coarse image-level annotations. Except for considering the performance of disease diagnosis, the study concentrates more on discovering the location of the pathological area which is used as visualised evidence for interpretability of diagnosis and the following retrospective analysis. To harvest more associated pathological areas, spatial non-local attention mechanism to learn non-local aware features is investigated. Further, a simple, effective, and widely applicable model ResNet-spatial non-local attention (SNA) is developed for these two objectives. Besides, an effective visualisation method compatible with the proposal is introduced. The effectiveness of the proposed ResNet-SNA was validated on the large publicly available chest X-ray dataset, ChestX-ray14. Compared with the baseline model, the proposed model improved by 7.96% averaged over 14 diseases, achieving 0.8247 area under the scores up to the highest classification results compared with related works. For localisation, the proposed model improved the performance significantly without using any extra information. More importantly, the proposal only requires image-level annotations without fine-grained expertise, which is cost-effective and expected to apply in clinical diagnosis.
Keywordfeature extraction diseases medical image processing patient diagnosis image segmentation image classification supervised learning image annotation thoracic disease diagnosis weakly supervised learning computer-aided diagnosis coarse image-level annotations pathological area nonlocal aware features fine-grained expertise clinical diagnosis visualisation method chest X-ray dataset spatial nonlocal attention mechanism ResNet-SNA
DOI10.1049/iet-ipr.2019.0032
WOS KeywordIMAGE SEGMENTATION
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61472423] ; National Natural Science Foundation of China[61602484] ; National Natural Science Foundation of China[61876183] ; Beijing Natural Science Foundation[4172063]
Funding OrganizationNational Natural Science Foundation of China ; Beijing Natural Science Foundation
WOS Research AreaComputer Science ; Engineering ; Imaging Science & Photographic Technology
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS IDWOS:000487789000014
PublisherINST ENGINEERING TECHNOLOGY-IET
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26981
Collection中国科学院自动化研究所
Corresponding AuthorZhang, Wensheng
Affiliation1.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Coll Artificial Intelligence, Beijing 101408, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yang, Menglin,Li, Ding,Zhang, Wensheng. Spatial non-local attention for thoracic disease diagnosis and visualisation in weakly supervised learning[J]. IET IMAGE PROCESSING,2019,13(11):1922-1930.
APA Yang, Menglin,Li, Ding,&Zhang, Wensheng.(2019).Spatial non-local attention for thoracic disease diagnosis and visualisation in weakly supervised learning.IET IMAGE PROCESSING,13(11),1922-1930.
MLA Yang, Menglin,et al."Spatial non-local attention for thoracic disease diagnosis and visualisation in weakly supervised learning".IET IMAGE PROCESSING 13.11(2019):1922-1930.
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