Spatial non-local attention for thoracic disease diagnosis and visualisation in weakly supervised learning
Yang, Menglin1,2; Li, Ding1; Zhang, Wensheng1,2
发表期刊IET IMAGE PROCESSING
ISSN1751-9659
2019-09-19
卷号13期号:11页码:1922-1930
通讯作者Zhang, Wensheng(zhangwenshengia@hotmail.com)
摘要Weakly 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.
关键词feature 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
关键词[WOS]IMAGE SEGMENTATION
收录类别SCI
语种英语
资助项目National 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] ; National 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]
项目资助者National Natural Science Foundation of China ; Beijing Natural Science Foundation
WOS研究方向Computer Science ; Engineering ; Imaging Science & Photographic Technology
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS记录号WOS:000487789000014
出版者INST ENGINEERING TECHNOLOGY-IET
七大方向——子方向分类人工智能+医疗
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/26981
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Zhang, Wensheng
作者单位1.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
第一作者单位精密感知与控制研究中心
通讯作者单位精密感知与控制研究中心
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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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