CardiacCLIP: Video-based CLIP Adaptation for LVEF Prediction in a Few-shot Manner

• URL: http://arxiv.org/abs/2509.17065v1
• Date: Sun, 21 Sep 2025 12:52:08 GMT
• Title: CardiacCLIP: Video-based CLIP Adaptation for LVEF Prediction in a Few-shot Manner
• Authors: Yao Du, Jiarong Guo, Xiaomeng Li,
• Abstract summary: Left ventricular ejection fraction (LVEF) serves as a key indicator of heart function.<n>Existing LVEF estimation methods depend on large-scale annotated video datasets.<n>We propose CardiacCLIP, a video-based framework that enhances LVEF prediction through attention-based frame aggregation and multi-resolution input scaling.
• Score: 14.429336783145644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Echocardiography is a vital non-invasive modality for cardiac assessment, with left ventricular ejection fraction (LVEF) serving as a key indicator of heart function. Existing LVEF estimation methods depend on large-scale annotated video datasets, which are costly and limit adaptability across various clinical settings. Recent vision-language models for echocardiography, such as EchoCLIP, apply image-to-text pretraining but fail to capture crucial temporal dynamics and localized cardiac structures essential for accurate diagnosis. To address these challenges, we propose CardiacCLIP, a video-based framework that enhances LVEF prediction through attention-based frame aggregation and multi-resolution input scaling. Specifically, we introduce MFL (Multi Frame Learning), a novel attention-based mechanism for selectively fusing informative frames, and EchoZoom, a multi-scale feature extraction strategy that refines spatial representations of cardiac structures. As a novel adaptation of CLIP models for few-shot echocardiogram video analysis, our approach significantly improves diagnostic accuracy, reducing MAE by 2.07 on the EchoNet-Dynamic dataset under 1-shot setting. The code is available at https://github.com/xmed-lab/CardiacCLIP.

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