2024-01-23-li24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Frozen Language Model Helps ECG Zero-Shot Learning	The electrocardiogram (ECG) is one of the most commonly used non-invasive, convenient medical monitoring tools that assist in the clinical diagnosis of heart diseases. Recently, deep learning (DL) techniques, particularly self-supervised learning (SSL), have demonstrated great potential in the classification of ECGs. SSL pre-training has achieved competitive performance with only a small amount of annotated data after fine-tuning. However, current SSL methods rely on the availability of annotated data and are unable to predict labels not existing in fine-tuning datasets. To address this challenge, we propose \textbf{M}ultimodal \textbf{E}CG-\textbf{T}ext \textbf{S}elf-supervised pre-training (METS), \textbf{the first work} to utilize the auto-generated clinical reports to guide ECG SSL pre-training. We use a trainable ECG encoder and a frozen language model to embed paired ECGs and automatically machine-generated clinical reports separately, then the ECG embedding and paired report embedding are compared with other unpaired embeddings. In downstream classification tasks, METS achieves around 10% improvement in performance without using any annotated data via zero-shot classification, compared to other supervised and SSL baselines that rely on annotated data. Furthermore, METS achieves the highest recall and F1 scores on the MIT-BIH dataset, despite MIT-BIH containing different classes of ECGs compared to the pre-trained dataset. The extensive experiments have demonstrated the advantages of using ECG-Text multimodal self-supervised learning in terms of generalizability and effectiveness.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	li24a	0	Frozen Language Model Helps ECG Zero-Shot Learning	402	415	402-415	402	false	Li, Jun and Liu, Che and Cheng, Sibo and Arcucci, Rossella and Hong, Shenda	given family Jun Li given family Che Liu given family Sibo Cheng given family Rossella Arcucci given family Shenda Hong	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/li24a/li24a.pdf