ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography
Published:Dec 3, 2025 01:11
•1 min read
•ArXiv
Analysis
This article introduces ProtoEFNet, a novel approach for estimating ejection fraction in echocardiography. The focus is on interpretability, suggesting the model aims to provide insights into its decision-making process. The use of dynamic prototype learning implies the model adapts its understanding of different cardiac conditions. The source being ArXiv indicates this is a research paper, likely detailing the methodology, results, and potential impact of ProtoEFNet.
Key Takeaways
- •ProtoEFNet is a new method for estimating ejection fraction in echocardiography.
- •The method emphasizes interpretability.
- •It utilizes dynamic prototype learning.
Reference
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