Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease.

ABSTRACT: Deep learning and computer vision algorithms can deliver highly accurate and automated interpretation of medical imaging to augment and assist clinicians. However, medical imaging presents uniquely pertinent obstacles such as a lack of accessible data or a high-cost of annotation. To address this, we developed data-efficient deep learning classifiers for prediction tasks in cardiology. Using pipeline supervised models to focus relevant structures, we achieve an accuracy of 94.4% for 15-view still-image echocardiographic view classification and 91.2% accuracy for binary left ventricular hypertrophy classification. We then develop semi-supervised generative adversarial network models that can learn from both labeled and unlabeled data in a generalizable fashion. We achieve greater than 80% a

SUBMITTER: Madani A 

PROVIDER: S-EPMC6550282 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature