Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study

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

arXiv · 2104.10553

Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study

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

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Abstract

Two DL models were developed using radiograph-level annotations (yes or no disease) and fine-grained lesion-level annotations (lesion bounding boxes), respectively named CheXNet and CheXDet. The models' internal classification performance and lesion localization performance were compared on a testing set (n=2,922), external classification performance was compared on NIH-Google (n=4,376) and PadChest (n=24,536) datasets, and external lesion localization performance was compared on NIH-ChestX-ray14 dataset (n=880). The models were also compared to radiologists on a subset of the internal testing set (n=496). Given sufficient training data, both models performed comparably to radiologists. CheXDet achieved significant improvement for external classification, such as in classifying fracture on NIH-Google (CheXDet area under the ROC curve [AUC]: 0.67, CheXNet AUC: 0.51; p<.001) and PadChest (CheXDet AUC: 0.78, CheXNet AUC: 0.55; p<.001). CheXDet achieved higher lesion detection performance than CheXNet for most abnormalities on all datasets, such as in detecting pneumothorax on the internal set (CheXDet jacknife alternative free-response ROC-figure of merit [JAFROC-FOM]: 0.87, CheXNet JAFROC-FOM: 0.13; p<.001) and NIH-ChestX-ray14 (CheXDet JAFROC-FOM: 0.55, CheXNet JAFROC-FOM: 0.04; p<.001). To summarize, fine-grained annotations overcame shortcut learning and enabled DL models to identify correct lesion patterns, improving the models' generalizability.

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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. 2021-04-21. Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study. https://arxiv.org/abs/2104.10553

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