ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography

Echocardiography is one of the most commonly used diagnostic imaging modalities in cardiology. Application of deep learning models to echocardiograms can enable automated identification of cardiac structures, estimation of cardiac function, and prediction of clinical outcomes. However, a major hindrance to realizing the full potential of deep learning is the lack of large-scale, fully curated and annotated data sets required for supervised training. High-quality pre-trained representations that can transfer useful visual features of echocardiograms to downstream tasks can help adapt deep learning models to new setups using fewer examples. In this paper, we design a suite of benchmarks that can be used to pre-train and evaluate echocardiographic representations with respect to various clinically-relevant tasks using publicly accessible data sets.