AEGFN: adaptive evidence-gated fusion network for medical image prediction via conflict compensation and DS evidence theory-driven regularization

The discrepancy in reliability and evidence conflict in multimodal data fusion for medical image prediction significantly undermines the accuracy of clinical decision-making. To address this challenge, we propose an Adaptive Evidence-Gated Fusion Network (AEGFN) based on Dempster-Shafer (DS) evidence theory. This framework models the evidence quality and cognitive uncertainty of CT images, image sequences, and clinical data using the Dirichlet distribution. We innovatively introduce an Evidence-Attention Gate (EAG) to dynamically adjust fusion weights for high-conflict modalities (conflict > 0.6), enabling conflict-aware uncertainty compensation. Additionally, a hybrid loss function combining KL divergence regularization with uncertainty-weighted cross-entropy is designed to balance model confidence and generalization. Evaluated on colorectal cancer (656 cases) and radiation pneumonitis (117 cases) datasets for binary classification tasks (predicting patient death and predicting RP occurrence), AEGFN achieves classification accuracies of 95.04% (AUC 0.97) and 82.34% (AUC 0.8312), outperforming the state-of-the-art method DDEF by 0.66% and 1.94%, respectively. This work provides a robust and interpretable solution for multimodal medical prediction, enhancing the reliability of clinical decision support systems.