AI Health Spark Seminar Series: Driving AI Innovation with Synthetic Data in Longitudinal Imaging, Continual Leaning, and Federated Learning for Healthcare
In this presentation, we explore the power of synthetic data in advancing healthcare AI applications, particularly focusing on longitudinal imaging, continual learning, and federated learning. The talk delves into two data synthesis methods: the diffusion model, a fine-grained approach, and datasets distillation, a coarse approach. First, I will begin by introducing our novel diffusion model-based method for longitudinal image synthesis, which has shown great promise in understanding brain aging through the generation of spatial-temporal data. This innovative technique allows for the efficient analysis of brain development and degeneration over time. Next, I will discuss the use of data distillation to enhance the performance of continual learning and federated learning. These learning paradigms address critical challenges in modern healthcare, such as data privacy constraints. Data distillation helps mitigate the catastrophic forgetting issue in continual learning and tackles the heterogeneity issue in federated learning, enabling more effective and adaptive AI solutions in healthcare. Through the use of synthetic data and cutting-edge AI methodologies, this presentation demonstrates the potential to advance healthcare by providing improved understanding, and diagnosis for patients, fostering collaboration, and ultimately improving patient outcomes.
This session is a part of the monthly seminar series organized by Spark: AI Health Initiative for Medical Imaging. The seminar will highlight outstanding work in medical imaging at Duke and beyond. The seminar recordings will be publicly available.
The Spark initiative focuses on development, validation, and clinical implementation of artificial intelligence algorithms for broadly understood medical imaging by bringing together the technical and clinical expertise across Duke campus. For more information please contact Dr. Maciej Mazurowski (firstname.lastname@example.org).