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Imaging of the Microstructural Failure Mechanism in the Human Hip
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Patch-based feature mapping with generative adversarial networks for auxiliary hip fracture detection.

Shang-Lin Chung1, Chi-Tung Cheng2, Chien-Hung Liao2

  • 1Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.

Computers in Biology and Medicine
|January 10, 2025
PubMed
Summary

This study introduces a patch-auxiliary generative adversarial network (PAGAN) to improve hip fracture detection in pelvic radiographs (PXRs). PAGAN enhances classification accuracy and model explainability by focusing on fracture regions.

Keywords:
Explainable AIGenerative adversarial networkHip fracture detectionWeakly supervised learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • Hip fractures are a major public health concern, especially in the elderly.
  • Pelvic radiographs (PXRs) are essential for diagnosing hip fractures.
  • Existing classification models for hip fracture detection sometimes lack explainability by focusing on non-fracture areas.

Purpose of the Study:

  • To improve the explainability of hip fracture detection models using weakly supervised learning.
  • To enhance the model's focus on the actual fracture region.
  • To introduce a quantitative method for evaluating the model's focus on the region of interest (ROI).

Main Methods:

  • Proposed a patch-auxiliary generative adversarial network (PAGAN) as an auxiliary module for classification models.
  • Integrated PAGAN with SOTA models like EfficientNetB0, ResNet50, and DenseNet121.
  • Utilized GradCAM for attention heatmaps and computed Intersection over Union (IOU) and Dice coefficient (Dise) to assess model explainability.

Main Results:

  • PAGAN integration improved classification accuracy for EfficientNetB0 (93.61% to 95.97%), ResNet50 (90.66% to 94.89%), and DenseNet121 (93.51% to 94.49%).
  • Model explainability, measured by IOU, significantly improved: EfficientNetB0 (0.32 to 0.54), ResNet50 (0.28 to 0.40), and DenseNet121 (0.37 to 0.51).

Conclusions:

  • The proposed PAGAN module enhances both the performance and explainability of hip fracture detection models.
  • PAGAN effectively directs model attention to the fracture region, improving diagnostic reliability.
  • This approach offers a valuable tool for improving AI-driven medical image analysis in orthopedics.