Multi Modal Magnetic Resonance

Multi-modal magnetic resonance imaging (MRI) leverages the complementary information from different MRI scans to improve medical image analysis, particularly in brain tumor segmentation and Alzheimer's disease diagnosis. Current research focuses on addressing challenges like missing modalities through generative models (e.g., diffusion models, GANs, autoencoders) and federated learning techniques to enable collaborative training while preserving patient privacy. These advancements, often employing transformer and U-Net architectures, aim to improve diagnostic accuracy and efficiency, ultimately leading to better patient care and more robust clinical decision-making.