MS-ANet: deep learning for automated multi-label thoracic disease detection and classification

doi:10.7717/peerj-cs.541

	MS-ANet: deep learning for automated multi-label thoracic disease detection and classification
	Xu, Jing 1; Li, Hui 2; Li, Xiu 1
	2021-05-17
发表期刊	PEERJ COMPUTER SCIENCE
ISSN	2376-5992
页码	12
摘要	The chest X-ray is one of the most common radiological examination types for the diagnosis of chest diseases. Nowadays, the automatic classification technology of radiological images has been widely used in clinical diagnosis and treatment plans. However, each disease has its own different response characteristic receptive field region, which is the main challenge for chest disease classification tasks. Besides, the imbalance of sample data categories further increases the difficulty of tasks. To solve these problems, we propose a new multi-label chest disease image classification scheme based on a multi-scale attention network. In this scheme, multi-scale information is iteratively fused to focus on regions with a high probability of disease, to effectively mine more meaningful information from data. A novel loss function is also designed to improve the rationality of visual perception and multi-label image classification, which forces the consistency of attention regions before and after image transformation. A comprehensive experiment was carried out on the Chest X-Ray14 and CheXpert datasets, separately containing over 100,000 frontal-view and 200,000 front and side view X-ray images with 14 diseases. The AUROC is 0.850 and 0.815 respectively on the two data sets, which achieve the state-of-the-art results, verified the effectiveness of this method in chest X-ray image classification. This study has important practical significance for using AI algorithms to assist radiologists in improving work efficiency and diagnostic accuracy.
关键词	Multi-label Chest X-Ray images Multi-Scale Attention Networks Image Classification
DOI	10.7717/peerj-cs.541
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[41876098] ; Shenzhen Science and Technology Project[JCYJ20200109143041798]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS记录号	WOS:000651853000001
出版者	PEERJ INC
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17715
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Xiu
作者单位	1.Tsinghua Univ, Shenzhen Int Grad Sch, Shenzhen, Guangdong, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Xu, Jing,Li, Hui,Li, Xiu. MS-ANet: deep learning for automated multi-label thoracic disease detection and classification[J]. PEERJ COMPUTER SCIENCE,2021:12.
APA	Xu, Jing,Li, Hui,&Li, Xiu.(2021).MS-ANet: deep learning for automated multi-label thoracic disease detection and classification.PEERJ COMPUTER SCIENCE,12.
MLA	Xu, Jing,et al."MS-ANet: deep learning for automated multi-label thoracic disease detection and classification".PEERJ COMPUTER SCIENCE (2021):12.