Code for paper MICCAI2022 paper "Evidence fusion with contextual discounting for multi-modality medical image segmentation".

We propose a new deep framework allowing us to merge multi-MRI image segmentation results using the formalism of Dempster-Shafer theory while taking into account the reliability of different modalities relative to different classes.

Environment requirement:

Before using the code, please install the required packages according to the instructions( refer to https://github.com/iWeisskohl/Evidential-neural-network-for-lymphoma-segmentation )

Models:

Copy the models from net  into ./monai/networks/nets

Pre-Trained weights of ES module for flair, t1, t1Gd and t2 are located in ./model_single_modality

Training: ./medical-segmentation-master_enn_fusion

python TRAINING_unet_enn.py

###########Citing this paper #############

@inproceedings{huang2022evidence,
  title={Evidence fusion with contextual discounting for multi-modality medical image segmentation},
  author={Huang, Ling and Denoeux, Thierry and Vera, Pierre and Ruan, Su},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={401--411},
  year={2022},
  organization={Springer}

}