Myocardial Infarction Detection with Incomplete Multi-View Data via Dual-Branch Gating Completion and Dirichlet Weighting

Multi-view myocardial infarction (MI) detection often relies on electrocardiogram (ECG) data, which suffers from noise sensitivity and limited early diagnostic value. Echocardiography provides richer temporal-spatial information but frequently encounters missing views due to clinical acquisition constraints. Existing fusion methods typically adopt static or overly complex dynamic weighting, limiting their adaptability to varying view quality and hindering real-time applicability. To this end, this paper proposes a view completion method based on a dual-branch gating structure, combining Transformer and Graph Neural Network (GNN) to complete incomplete multi-view information. Specifically, the model uses the Transformer encoder to model global temporal dependencies, introduces GNN to strengthen the local structural relationship between views, and uses the gating mechanism to achieve collaborative completion of the aforementioned two core components. Furthermore, this paper designs an uncertainty-driven dynamic weighted fusion strategy based on Dirichlet distribution, which can adaptively adjust the fusion weights according to the prediction confidence of each view, overcoming the limitations of traditional static weighting. Experiments on the HMC-QU dataset show that the proposed method achieves 92.31% accuracy, 90.00% precision, and 100.00% specificity, outperforming state-of-the-art models and demonstrating strong potential for clinical deployment.