Multi Site

Multi-site research focuses on analyzing data collected from multiple locations to improve the robustness and generalizability of models, particularly in healthcare and bioinformatics. Current research emphasizes developing methods to mitigate issues like catastrophic forgetting and site-specific biases, often employing deep learning architectures such as U-Nets, Bayesian networks, and attention-based models alongside techniques like federated learning and class-incremental learning. This work is crucial for building reliable and equitable AI systems in diverse settings, improving the accuracy of predictions in fields ranging from protein function prediction to medical image analysis.