Patch-based feature mapping with generative adversarial networks for auxiliary hip fracture detection.

Journal: Computers in biology and medicine
PMID: 39793347

Abstract

BACKGROUND: Hip fractures are a significant public health issue, particularly among the elderly population. Pelvic radiographs (PXRs) play a crucial role in diagnosing hip fractures and are commonly used for their evaluation. Previous research has demonstrated promising performance in classification models for hip fracture detection. However, these models sometimes focus on the images' non-fracture regions, reducing their explainability. This study applies weakly supervised learning techniques to address this issue and improve the model's focus on the fracture region. Additionally, we introduce a method to quantitatively evaluate the model's focus on the region of interest (ROI).

Authors

  • Shang-Lin Chung
    Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
  • Chi-Tung Cheng
    Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
  • Chien-Hung Liao
    Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan. surgymet@gmail.com.
  • I-Fang Chung
    Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.

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