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Binary View Classification of Echocardiograms of the Heart Using Transfer Learning

Echocardiogram metadata annotation is an entry level to the interpretation and analysis of echocardiographs for clinical usage. Automation of this task has been the subject of research over the past few years. In this paper we evaluate how far some state-of-the-art convolutional neural networks (CNNs) trained with non-radiological images can go at extracting features from cardiac structures in apical two-chamber (2CH) and four-chamber (4CH) echocardiograms for automatic binary view echocardiogram classification on a publicly available ultrasound dataset.