Summary of Echoprime: a Multi-video View-informed Vision-language Model For Comprehensive Echocardiography Interpretation, by Milos Vukadinovic et al.
EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation
by Milos Vukadinovic, Xiu Tang, Neal Yuan, Paul Cheng, Debiao Li, Susan Cheng, Bryan He, David Ouyang
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce EchoPrime, a multi-view video-based vision-language foundation model that leverages over 12 million video-report pairs to improve the accuracy and scope of applications for cardiac imaging. By utilizing contrastive learning and view-informed anatomic attention models, EchoPrime integrates information from multiple echocardiogram views to provide comprehensive clinical interpretations. The model achieves state-of-the-art performance on 23 benchmarks of cardiac form and function, surpassing task-specific approaches and prior foundation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EchoPrime is a new artificial intelligence tool that helps doctors analyze heart images more accurately. It uses special training data with over 12 million videos to learn how to look at heart images in different ways. The tool can help doctors quickly check for signs of heart disease or other problems by analyzing many heart image views together. |
Keywords
* Artificial intelligence * Attention
