多视角超声图像融合提升先天性心脏病诊断准确率
Trusted Multi-View Deep Learning Classification of Fetal Congenital Heart Disease with Feature-level and Decision-level Fusion
- 融合多角度超声图像特征与决策结果，提升诊断精度
- 在五视角数据集上达到顶尖分类性能，增强判别可靠性
- 适合医学影像分析、AI辅助诊断研究者参考
先天性心脏病（CHD）是胚胎发育过程中心脏及大血管解剖结构异常所致。传统诊断方法难以兼顾高准确率与高效率，尤其面对复杂心脏结构时更为困难。本研究提出一种专用于CHD二分类的多视角深度学习框架，基于包含五个视角的大型超声心动图数据集进行训练，实现多角度图像信息的融合。该框架采用先进的特征提取与注意力机制，提升诊断精确性与可靠性；同时引入基于不确定性的决策模块，有效处理低质量图像，优化诊断效果。实验表明，该方法在自建数据集上表现优异，具备临床应用潜力。论文接受后将公开数据集与源代码。
原文摘要 · Abstract (English)
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.
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