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

Luyang Luo1, Hao Chen1, Yongjie Xiao1

  • 1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China (L.L., Y.Z., X.W., H.L., P.A.H.); Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, 3/F Academic Building, Kowloon, Hong Kong, China (H.C.); AI Research Laboratory, Imsight Technology, Shenzhen, China (Y.X., H.L.); Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China (V.V.); Department of Radiology, Shenzhen People's Hospital, Luohu, Shenzhen, China (M.W.); Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (C.H., Z.L.); Department of Radiology, Queen Mary Hospital, Hong Kong, China (X.H.B.F.); Artificial Intelligence Laboratory, Head Office Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, China (E.T.); and Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (P.A.H.).

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Summary

Fine-grained annotations in deep learning (DL) models significantly improved diagnostic accuracy and lesion detection on chest radiographs. This approach overcomes shortcut learning, enhancing model generalizability for computer-aided diagnosis.

Keywords:
Computer-aided DiagnosisConventional RadiographyConvolutional Neural Network (CNN)Deep Learning AlgorithmsLocalizationMachine Learning Algorithms

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Computer-Aided Diagnosis

Background:

  • Deep learning (DL) models often exhibit shortcut learning, relying on superficial patterns rather than true diagnostic indicators in medical imaging.
  • Chest radiograph interpretation is complex, with potential for bias in automated diagnostic systems.
  • Fine-grained annotations, such as lesion localization, may offer a solution to improve DL model robustness.

Purpose of the Study:

  • To evaluate if fine-grained, lesion-level annotations can overcome shortcut learning in DL-based chest radiograph diagnosis.
  • To compare the performance of DL models trained with radiograph-level versus lesion-level annotations.
  • To assess the generalizability of these models on external datasets.

Main Methods:

  • Developed two DL models: CheXNet (radiograph-level annotations) and CheXDet (lesion-level bounding box annotations).
  • Utilized a large dataset of 34,501 chest radiographs with annotations for nine conditions.
  • Evaluated internal and external classification and lesion localization performance using ROC curve analysis and compared with radiologist performance.

Main Results:

  • CheXDet demonstrated superior external classification performance compared to CheXNet, notably for fractures on the NIH Google and PadChest datasets.
  • CheXDet achieved significantly higher lesion detection performance across multiple datasets and abnormalities, such as pneumothorax.
  • Both models performed comparably to radiologists when provided with sufficient training data.

Conclusions:

  • Fine-grained annotations effectively mitigate shortcut learning in DL models for chest radiography.
  • Lesion-level annotations enhance the ability of DL models to identify correct pathological patterns, improving diagnostic accuracy and generalizability.
  • This approach holds promise for developing more reliable and robust computer-aided diagnosis systems.