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arXiv · 2607.00748

FrameONE: Hierarchical Motion Modeling for Universal Multi-View Echocardiographic Keyframe Detection

Rusi Chen·Yuhao Huang·Hongyuan Zhang·Chao Tian·Shunan Ji·Yuhan Zhang·Dong Ni
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Abstract

Accurate detection of end-systole (ES) and end-diastole (ED) frames is fundamental to echocardiographic assessment. Existing methods are typically developed in a view-specific manner, depend on auxiliary annotations or intensive visual modeling, which limits their generalizability. In multi-view modeling, keyframe detection is driven by shared cardiac motion, yet large appearance differences and motion patterns make unified modeling challenging. To address these issues, we propose FrameONE, a unified end-to-end framework for multi-view echocardiographic keyframe detection. FrameONE introduces a Hierarchical Motion Modeling strategy: an intra-view multi-task learning reduces appearance bias and promotes motion-focused representations within each view; an inter-view general motion learning module further separates view-agnostic dynamics from view-specific patterns, enabling shared yet flexible motion representation learning across views. Extensive experiments on 25,872 videos spanning four standard views demonstrate that FrameONE achieves state-of-the-art keyframe detection accuracy with strong cross-view generalization. Code is available at https://github.com/szuboy/FrameONE.

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Rusi Chen, Yuhao Huang, Hongyuan Zhang, Chao Tian, Shunan Ji, Yuhan Zhang, Dong Ni. 2026-07-01. FrameONE: Hierarchical Motion Modeling for Universal Multi-View Echocardiographic Keyframe Detection. https://arxiv.org/abs/2607.00748

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