Ψ-Net: Triple-Branch Network with Cross-Branch Alternately Updated Fusion for Diagnosis of Bicuspid Aortic Valve Via Dual-View Echocardiography

Bicuspid Aortic Valve (BAV) can be diagnosed by Transthoracic Echocardiography (TTE), particularly on the parasternal short‑axis view. In this work, a Triple-Branch Network (named Ψ-Net) is proposed as a Computer-Aided Diagnosis (CAD) model for BAV based on the paired TTE images of aortic valve. This Ψ-shaped triple-branch network effectively learns both the view-common and view-specific features from the paired TTE images for improving feature representation. Moreover, a novel cross-branch alternately updated fusion block is developed by implementing alternately updated clique mechanism cross multiple branches, which maximizes cross-branch feature interaction among the Ψ-Net to enhance multi-view feature fusion. On the other hand, a multi-task self-supervised learning framework is developed to capture inherent properties from limited dual-view TTE samples by integrating the dual-view masked image modelling and Disentangled Representation Learning (DRL) into a unified framework. Specifically, an additional view classification task is designed and embedded into this framework for predicting which view a specific feature belongs to, so as to further promote the disentanglement learning of view-common and view-specific features by DRL. Moreover, the Shapley Value based weight adjustment strategy is designed to automatically assign weights to individual losses in objective function, which can dynamically balance the contribution of each loss term. The experimental results on two BAV TTE datasets demonstrate that Ψ-Net outperforms all the compared algorithms, suggesting its effectiveness in the diagnosis of BAV.