Do data-efficient semi-supervised deep learning models maintain high diagnostic accuracy for echocardiographic view and left ventricular hypertrophy classification while using significantly less labeled data?
Semi-supervised deep learning models can achieve high diagnostic accuracy for echocardiographic view and left ventricular hypertrophy classification while requiring significantly less annotated data, addressing a major bottleneck in medical AI development.
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% 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.
Madani et al. (Tue,) studied this question.