Trusted Multi-View Deep Learning Classification of Fetal Congenital Heart Disease with Feature-level and Decision-level Fusion

2026-06-13

AI Summary

This study addresses the challenge of balancing diagnostic accuracy and efficiency in detecting fetal congenital heart disease (CHD) amidst complex anatomical structures. To this end, the authors propose a multi-view deep learning framework that integrates five standard echocardiographic views. The approach combines feature-level and decision-level fusion strategies, incorporates an uncertainty-aware reliable decision module, and employs attention mechanisms to enhance perception of diagnostically critical regions. Experimental results on a large-scale, five-view fetal CHD dataset demonstrate that the model achieves high diagnostic accuracy while significantly improving robustness and interpretability. These advances collectively offer a reliable and intelligent tool to support early clinical screening of fetal CHD.

Abstract

Congenital heart disease (CHD) refers to the abnormal anatomical structure caused by the abnormal development of the heart and great vessels during embryonic development. Traditional diagnostics often fail to achieve high accuracy and efficiency, especially given the complexity of cardiac anatomy. This study presents a specialized multi-view deep learning framework for CHD binary classification using echocardiographic images. A large-scale CHD dataset, including five views, was used to train the model, enabling it to integrate multi-angle image data. The framework utilizes advanced feature extraction and attention mechanisms to improve diagnostic precision and reliability. An uncertainty-based decision-making component is also integrated to handle low-quality images, enhancing diagnostic outcomes. Experimental results show that this method achieves top-tier performance on our dataset and provides a robust tool for early CHD detection, underscoring its potential for clinical use. The dataset and source code will be released upon paper acceptance.

Problem

• Congenital Heart Disease
• Multi-View Classification
• Echocardiographic Images
• Diagnostic Accuracy
• Deep Learning

Innovation

• multi-view fusion
• feature-level fusion
• decision-level fusion
• uncertainty-aware classification
• attention mechanism