Prior work lacks explainability or is limited in scope by focusing on a single cardiovascular task. In addition, such methods must be able to process several echo videos obtained from various heart views and the interactions among them to properly produce predictions for a variety of cardiovascular measurements or interpretation tasks. For such safety-critical applications, it is essential for any proposed ML method to present a level of explainability along with good accuracy. Due to inter-observer variability in echo-based diagnosis, which arises from the variability in echo image acquisition and the interpretation of echo images based on clinical experience, vision-based machine learning (ML) methods have gained popularity to act as secondary layers of verification. Echocardiography (echo) is an ultrasound imaging modality that is widely used for various cardiovascular diagnosis tasks.
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