Described herein are means for the generation of semantic genesis models through self-supervised learning in the absence of manual labeling, in which the trained semantic genesis models are then utilized for the processing of medical imaging. For instance, an exemplary system is specially configured with means for performing a self-discovery operation which crops 2D patches or crops 3D cubes from similar patient scans received at the system as input; means for transforming each anatomical pattern represented within the cropped 2D patches or the cropped 3D cubes to generate transformed 2D anatomical patterns or transformed 3D anatomical patterns; means for performing a self-classification operation of the transformed anatomical patterns by formulating a C-way multi-class classification task for representation learning; means for performing a self-restoration operation by recovering original anatomical patterns from the transformed 2D patches or transformed 3D cubes having transformed anatomical patterns embedded therein to learn different sets of visual representation; and means for providing a semantics-enriched pre-trained AI model having a trained encoder-decoder structure with skip connections in between based on the performance of the self-discovery operation, the self-classification operation, and the self-restoration operation. Other related embodiments are disclosed.
SYSTEMS, METHODS, AND APPARATUSES FOR THE USE OF TRANSFERABLE VISUAL WORDS FOR AI MODELS THROUGH SELF-SUPERVISED LEARNING IN THE ABSENCE OF MANUAL LABELING FOR THE PROCESSING OF MEDICAL IMAGING