Abstract: Embodiments described herein provide for training an artificial intelligence model to become a preference-aware model. The artificial intelligence model preferences as the artificial intelligence model trains. Reinforcement learning is used to train experts in the artificial intelligence model such that each expert is trained to converge to a unique preference. The architecture of the artificial intelligence model is highly flexible. Upon executing a trained model, users can select automatically images according to various preferences based on medical professional preferences, geographic preferences, patient anatomy, and institutional guidelines.

Abstract: A system can include at least one computing module comprising a physical interface for connection to a memory bus, a processing section configured to decode at least a predetermined range of physical address signals received over the memory bus into computing instructions for the computing module, and at least one computing element configured to execute the computing instructions.

Abstract: A method that decodes serially received MPEG variable length codes by executing instructions in parallel. The method includes an execution unit which includes multiple pipelined functional units. The functional units execute at least two of the instructions in parallel. The instructions utilize and share general purpose. registers. The general purpose. registers store information used by at least two of the instructions.