Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images

@article{Deng2022CrossscaleAG,
  title={Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images},
  author={Ruining Deng and Can Cui and Lucas W. Remedios and Shunxing Bao and R. Michael Womick and Sophie Chiron and Jia Li and Joseph T. Roland and Ken S. Lau and Qi Liu and Keith T. Wilson and Yao Wang and Lori A. Coburn and Bennett A. Landman and Yuankai Huo},
  journal={Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings},
  year={2022},
  volume={13594},
  pages={
          24-33
        }
}
  • Ruining DengC. Cui Yuankai Huo
  • Published 15 August 2022
  • Computer Science
  • Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings
Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations. Often, this approach directly applies "natural image driven" MIL algorithms which overlook the multi-scale (i.e. pyramidal) nature of WSIs. Off-the-shelf MIL algorithms are typically deployed on a single-scale of WSIs (e.g., 20× magnification), while human pathologists usually aggregate the global and local… 

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