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.

翻译：先天性心脏病（CHD）是指胚胎发育期间心脏及大血管发育异常所导致的解剖结构畸形。传统诊断方法因心脏解剖结构的复杂性，往往难以达到高精度和高效率。本研究提出了一种专门用于超声心动图图像进行CHD二分类的多视角深度学习框架。我们利用包含五个视角的大规模CHD数据集训练模型，使其能够集成多角度图像数据。该框架采用先进的特征提取与注意力机制，提升了诊断精度与可靠性。此外，还集成了基于不确定性的决策模块以处理低质量图像，从而改善诊断结果。实验结果表明，该方法在数据集上取得了顶尖性能，为CHD早期检测提供了稳健工具，凸显了其在临床应用中的潜力。数据集与源代码将在论文接收后发布。