# Motion-Conditioned Multi-View Fusion for Myocardial Infarction Localization from Echocardiography

arXiv: https://arxiv.org/abs/2607.15268 · HTML: https://mlsanity.com/paper/2607.15268/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Builds on (most cited first, 11 of 11)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos](https://mlsanity.com/paper/2410.11831/) (2024)
- [Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI](https://mlsanity.com/paper/2006.04725/) (2020)
- [Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection](https://mlsanity.com/paper/2008.04615/) (2020)
- [EchoFM: Foundation Model for Generalizable Echocardiogram Analysis](https://mlsanity.com/paper/2410.23413/) (2024)
- [Early Myocardial Infarction Detection over Multi-view Echocardiography](https://mlsanity.com/paper/2111.05790/) (2021)
- [Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusion](https://mlsanity.com/paper/2501.05241/) (2025)
- [Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI with Explicit Cardiac Motion Modeling](https://mlsanity.com/paper/2507.15194/) (2025)

## Abstract (arXiv)

Myocardial infarction (MI) remains a leading cause of mortality worldwide. Echocardiography (Echo) is a widely available modality for MI assessment, where regional wall motion abnormality is a key indicator. Prior learning based methods for myocardial motion analysis often use handcrafted descriptors or densely supervised estimation, but the need for extensive annotation limits applicability. Foundation models have recently improved vision-based Echo analysis; however, most methods operate on single views and segment-level localization remains unreliable under view-dependent ambiguity, especially in apical views. To address this, we propose MCF-Net, a novel motion-guided multi-view fusion framework that fuses myocardial motion cues with foundation model representations to localize infarction. Visual features are extracted using EchoPrime, a pretrained Echo foundation model shared across dual views. Cardiac motion is modeled with extremely sparse supervision: a single annotated template frame is transferred across videos to initialize point tracking, avoiding dense labels. Motion-derived segment-aware soft masks provide coarse spatial priors that selectively enhance features for challenging myocardial segments. A motion-conditioned fusion mechanism then integrates motion and vision across views, refining predictions without overriding strong appearance cues. On segment-level MI localization, MCF-Net achieves 72.4% F1 and 84.9% accuracy, outperforming state-of-the-art motion-only, vision-only, and fusion baselines.