Incremental Learning Versus Batch Learning for Classification of User's Behaviour in Medical Imaging

Communication latency still hinders the adoption of Cloud computing paradigms in medical imaging environments where it could serve as a reliable technology to support repository outsourcing solutions or inter-institutional workflows, for instance. One way to overcome this is by implementing cache repositories and prefetching mechanisms. Nevertheless, such solutions are usually based on static rules that may inefficiently manage the cache storage capacity. For that reason, this paper compares a pattern recognition system using incremental learning versus batch learning, in order to assess which one could be more appropriately used in a medical imaging cache mechanism.