Described herein are means for learning semantics-enriched representations via self-discovery, self-classification, and self-restoration in the context of medical imaging. Embodiments include the training of deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a collection of semantics-enriched pre-trained models, called Semantic Genesis. Other related embodiments are disclosed.
SYSTEMS, METHODS, AND APPARATUSES FOR LEARNING SEMANTICS-ENRICHED REPRESENTATIONS VIA SELF-DISCOVERY, SELF-CLASSIFICATION, AND SELF-RESTORATION IN THE CONTEXT OF MEDICAL IMAGING