Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation

Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux

Abstract: Highlights•A new deep framework for multi-modality medical image segmentation is introduced.•The approach is based on Dempster–Shafer theory of evidence.•Mass functions are transformed by contextual discounting with learnt coefficients.•The discounting coefficients provide information about the fusion process.•The model performance is assessed using two PET-CT and multi-MRI datasets.