比较深度模型对扫描心电图进行多标签诊断的性能
Comparing Deep Neural Network for Multi-Label ECG Diagnosis From Scanned ECG
- 用多种深度网络分析扫描心电图图像，实现多标签分类
- 发现不同模型在抗图像噪声和跨场景泛化上表现差异明显
- 适合关注医学图像自动化诊断的研究者与临床工程师
自动化心电图诊断在深度学习推动下取得显著进展，但在实际应用中仍面临扫描纸质心电图带来的挑战。本研究探讨从扫描图像中提取的心电图进行多标签分类的可行性，突破传统二分类（正常/异常）限制。我们评估了AlexNet、VGG、ResNet和Vision Transformer等多种深度神经网络架构在扫描心电图数据集上的表现，重点比较其分类准确率、对图像伪影的鲁棒性以及在不同心电图条件下的泛化能力。同时，探究扫描图像中提取的心电信号是否保留足够诊断信息以支持可靠自动分类。结果揭示了各模型的优势与局限，为基于图像的心电图诊断可行性及其在临床工作流中的整合提供了重要参考。
原文摘要 · Abstract (English)
Automated ECG diagnosis has seen significant advancements with deep learning techniques, but real-world applications still face challenges when dealing with scanned paper ECGs. In this study, we explore multi-label classification of ECGs extracted from scanned images, moving beyond traditional binary classification (normal/abnormal). We evaluate the performance of multiple deep neural network architectures, including AlexNet, VGG, ResNet, and Vision Transformer, on scanned ECG datasets. Our comparative analysis examines model accuracy, robustness to image artifacts, and generalizability across different ECG conditions. Additionally, we investigate whether ECG signals extracted from scanned images retain sufficient diagnostic information for reliable automated classification. The findings highlight the strengths and limitations of each architecture, providing insights into the feasibility of image-based ECG diagnosis and its potential integration into clinical workflows.
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