基于Jaccard分数的Sigmoid损失的多标签ECG分类对比学习
Contrastive Learning for Multi Label ECG Classification with Jaccard Score Based Sigmoid Loss
AI总结：
本研究提出基于Jaccard分数的Sigmoid损失的对比学习方法，用于提升多标签ECG分类的性能，通过改进损失函数和数据增强技术提高诊断准确性。
摘要：
近年来，大语言模型（LLMs）的发展使得多模态医疗AI的开发成为可能。尽管像MedGemini这样的模型在USMLE MM等VQA任务上表现出色，但其在基于ECG的任务上的表现仍然有限，一些模型如MedGemma根本不支持ECG数据。解读ECG固然是具有挑战性的，诊断准确性会根据解释者的经验而有所不同。虽然超声心动图提供了丰富的诊断信息，但它需要专门的设备和人员，限制了其可用性。在本研究中，我们专注于使用真实世界医院数据构建稳健的ECG编码器以进行多模态预训练。我们采用SigLIP，一种基于CLIP的模型，其采用基于Sigmoid的损失函数以实现多标签预测，并引入一种针对ECG数据多标签性质进行修改的损失函数。实验表明，将医学知识融入语言模型并应用修改后的损失显著提高了多标签ECG分类。为了进一步提高性能，我们增加了嵌入维度并应用随机裁剪以缓解数据漂移。最后，对每个标签的分析揭示了哪些ECG发现更容易或更难预测。我们的研究为开发利用ECG数据的医疗模型提供了基础框架。
英文摘要：
Recent advances in large language models (LLMs) have enabled the development of multimodal medical AI. While models such as MedGemini achieve high accuracy on VQA tasks like USMLE MM, their performance on ECG based tasks remains limited, and some models, such as MedGemma, do not support ECG data at all. Interpreting ECGs is inherently challenging, and diagnostic accuracy can vary depending on the interpreter's experience. Although echocardiography provides rich diagnostic information, it requires specialized equipment and personnel, limiting its availability. In this study, we focus on constructing a robust ECG encoder for multimodal pretraining using real world hospital data. We employ SigLIP, a CLIP based model with a sigmoid based loss function enabling multi label prediction, and introduce a modified loss function tailored to the multi label nature of ECG data. Experiments demonstrate that incorporating medical knowledge in the language model and applying the modified loss significantly improve multi label ECG classification. To further enhance performance, we increase the embedding dimensionality and apply random cropping to mitigate data drift. Finally, per label analysis reveals which ECG findings are easier or harder to predict. Our study provides a foundational framework for developing medical models that utilize ECG data.
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