Echo-Vision-FM: a pre-training and fine-tuning framework for echocardiogram video vision foundation model.

ABSTRACT: We introduce and evaluate Echo-Vision-FM (Echocardiogram Video Vision Foundation Model), a self-supervised video learning framework designed to pre-train a video encoder on large-scale, unlabeled echocardiogram data. The framework aims to generate robust, transferable video representations that enhance downstream performance across diverse echocardiogram datasets and clinical scenarios. Leveraging the publicly available MIMIC-IV-ECHO dataset, we employ an advanced masked auto-encoding strategy with 85% mask ratio to pre-train our echo-video encoder without requiring manual annotations. To further improve the learned representations, we introduce Spatial-Temporal Fusion Network (STF-Net), which integrates spatial and temporal correlations from the learned video representations through dual

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC12764486 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature