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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% accuracy in view classification with only 4% of labeled data used in solely supervised techniques and achieve 92.3% accuracy for left ventricular hypertrophy classification. In exploring trade-offs between model type, resolution, data resources, and performance, we present a comprehensive analysis and improvements of efficient deep learning solutions for medical imaging assessment especially in cardiology.<\/jats:p>","DOI":"10.1038\/s41746-018-0065-x","type":"journal-article","created":{"date-parts":[[2018,10,9]],"date-time":"2018-10-09T16:13:03Z","timestamp":1539101583000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":214,"title":["Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease"],"prefix":"10.1038","volume":"1","author":[{"given":"Ali","family":"Madani","sequence":"first","affiliation":[]},{"given":"Jia Rui","family":"Ong","sequence":"additional","affiliation":[]},{"given":"Anshul","family":"Tibrewal","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7004-4859","authenticated-orcid":false,"given":"Mohammad R. 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