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Results for "DEFUSE-MS"

When AI stops playing “spot the difference” and starts understanding changes in MRIs

MBZUAI ·

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.

Multi-Omics Data Fusion for Enabling Precision Medicine

MBZUAI ·

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.

Deep Surface Meshes

MBZUAI ·

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.

Developing disposable lifesaving sensors

KAUST ·

KAUST researchers led by Atif Shamim have developed a low-cost, 3D-printed wireless sensor node for real-time environmental monitoring. The disposable sensor nodes can detect noxious gases, temperature, and humidity, and have been tested in the lab and field, surviving drops and temperatures up to 70°C. The system aims to saturate high-risk areas with these sensors, linked wirelessly to fixed nodes that raise alarms. Why it matters: This innovation provides a cost-effective solution for large-scale environmental monitoring, addressing the limitations of expensive fixed sensors and satellite monitoring, and potentially revolutionizing early warning systems for wildfires and gas leaks in the region.

DERC New Partnerships

TII ·

The Directed Energy Research Center (DERC) is partnering with Montena Technology to study high-altitude electromagnetic pulses and design infrastructure safeguards. DERC is also collaborating with Radaz to evaluate ground penetrating and synthetic aperture radars in Abu Dhabi, aiming to identify natural resources. Additionally, DERC and Université de Picardie Jules Verne are working on laser sources and sensors, with a DERC researcher spending four years in France. Why it matters: These partnerships enhance DERC's research capabilities in critical areas like infrastructure protection, resource exploration, and advanced sensing technologies.