Single Center Training

Single-center training in machine learning focuses on developing models using data from a single source, addressing challenges like data scarcity and privacy concerns in multi-center collaborations. Current research explores techniques like self-supervised pre-training to efficiently generate models of varying sizes from a single training run, and transfer learning methods such as learning without forgetting to improve model generalizability across different datasets without compromising data privacy. These advancements are significant for improving the efficiency and robustness of machine learning models in various applications, particularly in healthcare where data sharing is often restricted.