ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography

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.

• •ProtoEFNet is a new method for estimating ejection fraction in echocardiography.
• •The method emphasizes interpretability.
• •It utilizes dynamic prototype learning.