A Self-guided Framework for Radiology Report Generation

被引:11
|
作者
Li, Jun [1 ,2 ]
Li, Shibo [1 ]
Hu, Ying [1 ]
Tao, Huiren [3 ]
机构
[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
[2] Univ Chinese Acad Sci, Beijing, Peoples R China
[3] Shenzhen Univ, Gen Hosp, Shenzhen, Peoples R China
来源
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VIII | 2022年 / 13438卷
基金
中国国家自然科学基金;
关键词
Report generation; Data bias; Unsupervised learning; Clustering;
D O I
10.1007/978-3-031-16452-1_56
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Automatic radiology report generation is essential to computer-aided diagnosis. Through the success of image captioning, medical report generation has been achievable. However, the lack of annotated disease labels is still the bottleneck of this area. In addition, the image-text data bias problem and complex sentences make it more difficult to generate accurate reports. To address these gaps, we present a self-guided framework (SGF), a suite of unsupervised and supervised deep learning methods to mimic the process of human learning and writing. In detail, our framework obtains the domain knowledge from medical reports without extra disease labels and guides itself to extract fined-grain visual features associated with the text. Moreover, SGF successfully improves the accuracy and length of medical report generation by incorporating a similarity comparison mechanism that imitates the process of human self-improvement through comparative practice. Extensive experiments demonstrate the utility of our SGF in the majority of cases, showing its superior performance over state-of-the-art methods. Our results highlight the capacity of the proposed framework to distinguish fined-grained visual details between words and verify its advantage in generating medical reports.
引用
收藏
页码:588 / 598
页数:11
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