Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays

@article{Han2022RadiomicsGuidedGT,
  title={Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays},
  author={Yan Han and Greg Holste and Ying Ding and Ahmed Tewfik and Yifan Peng and Zhangyang Wang},
  journal={IEEE transactions on medical imaging},
  year={2022},
  volume={PP}
}
Before the recent success of deep learning methods for automated medical image analysis, practitioners used handcrafted radiomic features to quantitatively describe local patches of medical images. However, extracting discriminative radiomic features relies on accurate pathology localization, which is difficult to acquire in real-world settings. Despite advances in disease classification and localization from chest X-rays, many approaches fail to incorporate clinically-informed domain-specific… 
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