EchoApex: A General-Purpose Vision Foundation Model for Echocardiography
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 ventricle hypertrophy detection and automated ejection fraction estimation from view sequences. Compared to state-of-the-art task-specific models, EchoApex attains improved performance with a unified image encoding architecture, demonstrating the benefits of model pretraining at scale with in-domain data. Furthermore, EchoApex illustrates the potential for developing a general-purpose vision foundation model tailored specifically for echocardiography, capable of addressing a diverse range of clinical applications with high efficiency and efficacy.
Code (0)
등록된 구현이 없습니다.
Tasks
Self-Supervised LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
CardioBench: Do Echocardiography Foundation Models Generalize Beyond the Lab?
Foundation models are reshaping medical imaging, yet their application in echocardiography remains limited, hindered by a heavy reliance on private datasets that prevent reproducible comparison. Echocardiography poses un…
EchoFM: Foundation Model for Generalizable Echocardiogram Analysis
Foundation models have recently gained significant attention because of their generalizability and adaptability across multiple tasks and data distributions. Although medical foundation models have emerged, solutions for…
Contrastive LearningmodelSelf-Supervised LearningEchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation
Echocardiography is the most widely used cardiac imaging modality, capturing ultrasound video data to assess cardiac structure and function. Artificial intelligence (AI) in echocardiography has the potential to streamlin…
Contrastive LearningLanguage ModelingLanguage ModellingMultimodal Foundation Models For Echocardiogram Interpretation
Multimodal deep learning foundation models can learn the relationship between images and text. In the context of medical imaging, mapping images to language concepts reflects the clinical task of diagnostic image interpr…
Cross-Modal RetrievalDiagnosticImage to textMultimodal Deep Learning+1Echocardiography to Cardiac MRI View Transformation for Real-Time Blind Restoration
Echocardiography is the most widely used imaging to monitor cardiac functions, serving as the first line in early detection of myocardial ischemia and infarction. However, echocardiography often suffers from several arti…
DenoisingGenerative Adversarial Network