Explore a comprehensive lecture on training medical image segmentation models with reduced labeled data requirements. Delve into Sarah Hooper's research at Stanford University, focusing on a semi-supervised method that significantly decreases the need for extensive labeled datasets in neural network training for medical image segmentation. Learn about the application of this technique to cardiac magnetic resonance (CMR) segmentation, its impact on deriving cardiac functional biomarkers, and the potential for making quality healthcare more accessible. Gain insights into the two-step process involving data augmentation, traditional supervision, and self-supervised learning, as well as the evaluation of labeling reduction, error modes, and generalization capabilities of the proposed model.