Multi-Modal Knowledge Distillation for Pathological Glioma Grading

by Xiaohan Xing.

This repository contains official PyTorch implementations of the paper

1. "Discrepancy and Gradient-guided Multi-Modal Knowledge Distillation for Pathological Glioma Grading" from MICCAI 2022.
2. "Gradient modulated contrastive distillation of low-rank multi-modal knowledge for disease diagnosis" from MIA 2023.
3. "Comprehensive Learning and Adaptive Teaching: Distilling Multi-Modal Knowledge for Pathological Glioma Grading" from MIA 2023.

Citation:

@inproceedings{xing2022discrepancy,
  title={Discrepancy and Gradient-Guided Multi-modal Knowledge Distillation for Pathological Glioma Grading},
  author={Xing, Xiaohan and Chen, Zhen and Zhu, Meilu and Hou, Yuenan and Gao, Zhifan and Yuan, Yixuan},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={636--646},
  year={2022},
  organization={Springer}
}
@article{xing2023gradient,
  title={Gradient modulated contrastive distillation of low-rank multi-modal knowledge for disease diagnosis},
  author={Xing, Xiaohan and Chen, Zhen and Hou, Yuenan and Yuan, Yixuan},
  journal={Medical Image Analysis},
  pages={102874},
  year={2023},
  publisher={Elsevier}
}

Acknowledgement:

The code is based on Pathomic Fusion and Contrastive Knowledge Distillation (CRD). For enquiries, please contact "[email protected]"