MBZUAI researchers presented DEFUSE-MS at MICCAI 2025, a novel AI system for analyzing changes in MRI scans of multiple sclerosis (MS) patients. DEFUSE-MS uses a deformation field-guided spatiotemporal graph-based framework to identify new lesions by reasoning about how the brain has changed. The model constructs graphs of small regions within baseline and follow-up MRIs, linking them across time with edges enriched with learned embeddings of the deformation field. Why it matters: DEFUSE-MS reframes the task from simple "spot the difference" to understanding structural changes, potentially improving the speed and accuracy of MS diagnosis and treatment monitoring.
The National Multiple Sclerosis Society has awarded funding to four research projects aimed at advancing multiple sclerosis research within the UAE. This initiative marks the third cycle of their Research Grant Programme. The funding supports local scientists and institutions in conducting studies related to the neurological disorder. Why it matters: This commitment strengthens the UAE's capacity in medical research, fostering local scientific innovation and addressing critical health challenges in the region.
Natasa Przulj at the Barcelona Supercomputing Center is developing an AI framework that fuses multi-omic data to improve precision medicine. The framework uses graph-regularized non-negative matrix tri-factorization (NMTF) and network science algorithms for patient stratification, biomarker prediction, and drug repurposing. It is applied to diseases like cancer, Covid-19, and Parkinson's. Why it matters: This research can enable more personalized and effective treatments by leveraging complex biological data to understand disease mechanisms and tailor therapies.
Pascal Fua from EPFL presented an approach to implementing convolutional neural nets that output complex 3D surface meshes. The method overcomes limitations in converting implicit representations to explicit surface representations. Applications include single view reconstruction, physically-driven shape optimization, and bio-medical image segmentation. Why it matters: This research advances geometric deep learning by enabling end-to-end trainable models for 3D surface mesh generation, with potential impact on various applications in computer vision and biomedical imaging in the region.