直接从打印心电图图像诊断心脏病，无需数字化转换
Pic2Diagnosis: A Method for Diagnosis of Cardiovascular Diseases from the Printed ECG Pictures
- 用两阶段课程学习框架，先用分割掩码预训练，再在灰度反转图像上微调
- 在BHF心电挑战数据集上达AUC 0.9534、F1 0.7801，优于单模型
- 适合资源匮乏地区，可快速处理扫描或打印的心电图
心电图（ECG）是诊断心脏疾病的重要工具，但许多疾病模式基于过时数据集和传统分步算法，准确率有限。本研究提出一种直接从心电图图像进行心血管疾病（CVD）诊断的方法，无需数字化转换。该方法采用两阶段课程学习框架：首先在分割掩码上预训练分类模型，随后在灰度反转的ECG图像上进行微调。通过集成三个模型并取平均输出，进一步提升鲁棒性，在BHF心电挑战数据集上实现AUC 0.9534和F1分数0.7801，优于单个模型。该方法有效处理真实世界中的伪影，简化诊断流程，为自动化CVD诊断提供可靠方案，尤其适用于广泛使用打印或扫描心电图的资源匮乏地区。自动化流程可实现快速准确诊断，对常需紧急干预的心血管疾病至关重要。
原文摘要 · Abstract (English)
The electrocardiogram (ECG) is a vital tool for diagnosing heart diseases. However, many disease patterns are derived from outdated datasets and traditional stepwise algorithms with limited accuracy. This study presents a method for direct cardiovascular disease (CVD) diagnosis from ECG images, eliminating the need for digitization. The proposed approach utilizes a two-step curriculum learning framework, beginning with the pre-training of a classification model on segmentation masks, followed by fine-tuning on grayscale, inverted ECG images. Robustness is further enhanced through an ensemble of three models with averaged outputs, achieving an AUC of 0.9534 and an F1 score of 0.7801 on the BHF ECG Challenge dataset, outperforming individual models. By effectively handling real-world artifacts and simplifying the diagnostic process, this method offers a reliable solution for automated CVD diagnosis, particularly in resource-limited settings where printed or scanned ECG images are commonly used. Such an automated procedure enables rapid and accurate diagnosis, which is critical for timely intervention in CVD cases that often demand urgent care.
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