Multi Modality Imaging

Multi-modality imaging integrates data from multiple imaging sources (e.g., MRI, CT, PET) to provide a more comprehensive understanding of biological systems than any single modality could offer. Current research focuses on developing advanced computational methods, including graph neural networks, variational autoencoders, and generative adversarial networks, to effectively fuse heterogeneous data, address modality-specific challenges (like dimensionality mismatches), and improve the accuracy of downstream tasks such as segmentation, classification, and image synthesis. This approach holds significant promise for improving medical diagnoses, particularly in oncology and neurology, by enabling more accurate and personalized treatment planning and enhancing our understanding of complex biological processes.