EchoApex: A General-Purpose Vision Foundation Model for Echocardiography
Abdoul Aziz Amadou, Yue Zhang, Sebastien Piat, Paul Klein, Ingo, Schmuecking, Tiziano Passerini, Puneet Sharma

TL;DR
EchoApex is a pioneering general-purpose vision foundation model for echocardiography, trained on over 20 million images, capable of handling diverse clinical tasks with improved performance and efficiency.
Contribution
It introduces the first large-scale, self-supervised pretrained vision model specifically designed for echocardiography, enabling versatile clinical applications.
Findings
Outperforms task-specific models across multiple echocardiography tasks.
Demonstrates high accuracy in view classification and structure segmentation.
Shows potential for broad clinical adoption with a unified architecture.
Abstract
Quantitative evaluation of echocardiography is essential for precise assessment of cardiac condition, monitoring disease progression, and guiding treatment decisions. The diverse nature of echo images, including variations in probe types, manufacturers, and pathologies, poses challenges for developing artificial intelligent models that can generalize across different clinical practice. We introduce EchoApex, the first general-purpose vision foundation model echocardiography with applications on a variety of clinical practice. Leveraging self-supervised learning, EchoApex is pretrained on over 20 million echo images from 11 clinical centres. By incorporating task-specific decoders and adapter modules, we demonstrate the effectiveness of EchoApex on 4 different kind of clinical applications with 28 sub-tasks, including view classification, interactive structure segmentation, left…
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Taxonomy
TopicsMedical Image Segmentation Techniques
MethodsAdapter
