作者
Luyang Luo, Hao Chen, Yongjie Xiao, Yanning Zhou, Xi Wang, Varut Vardhanabhuti, Mingxiang Wu, Chu Han, Zaiyi Liu, Xin Hao Benjamin Fang, Efstratios Tsougenis, Huangjing Lin, Pheng-Ann Heng
发表日期
2022/7/20
期刊
Radiology: Artificial Intelligence
卷号
4
期号
5
页码范围
e210299
出版商
Radiological Society of North America
简介
Purpose
To evaluate the ability of fine-grained annotations to overcome shortcut learning in deep learning (DL)–based diagnosis using chest radiographs.
Materials and Methods
Two DL models were developed using radiograph-level annotations (disease present: yes or no) and fine-grained lesion-level annotations (lesion bounding boxes), respectively named CheXNet and CheXDet. A total of 34 501 chest radiographs obtained from January 2005 to September 2019 were retrospectively collected and annotated regarding cardiomegaly, pleural effusion, mass, nodule, pneumonia, pneumothorax, tuberculosis, fracture, and aortic calcification. The internal classification performance and …
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L Luo, H Chen, Y Xiao, Y Zhou, X Wang… - Radiology: Artificial Intelligence, 2022