MS-ANet: deep learning for automated multi-label thoracic disease detection and classification
文献类型：期刊论文
| 作者 | Xu, Jing2; Li, Hui1; Li, Xiu2 |
| 刊名 | PEERJ COMPUTER SCIENCE
 |
| 出版日期 | 2021-05-17 |
| 页码 | 12 |
| 关键词 | Multi-label Chest X-Ray images Multi-Scale Attention Networks Image Classification |
| ISSN号 | 2376-5992 |
| DOI | 10.7717/peerj-cs.541 |
| 英文摘要 | 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. |
| 资助项目 | National Natural Science Foundation of China[41876098] ; Shenzhen Science and Technology Project[JCYJ20200109143041798] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000651853000001 |
| 出版者 | PEERJ INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/17714]  |
| 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.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Tsinghua Univ, Shenzhen Int Grad Sch, Shenzhen, Guangdong, 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. |
入库方式： OAI收割
来源：计算技术研究所
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