Multi Center

Multi-center research focuses on developing and validating machine learning models that perform reliably across diverse datasets from multiple medical institutions, overcoming data heterogeneity and privacy limitations. Current efforts concentrate on improving model generalizability using techniques like federated learning, transfer learning (including "learning without forgetting"), and data augmentation, often employing architectures such as U-Nets, CycleGANs, and various transformer-based models. This work is crucial for advancing the clinical applicability of AI in healthcare, enabling the development of robust and reliable diagnostic and prognostic tools that can be deployed in various settings without compromising patient data privacy.