Researchers from MBZUAI have developed MMRINet, a Mamba-based neural network for efficient brain tumor segmentation in MRI scans. The model uses Dual-Path Feature Refinement and Progressive Feature Aggregation to achieve high accuracy with only 2.5M parameters, making it suitable for low-resource clinical environments. MMRINet achieves a Dice score of 0.752 and HD95 of 12.23 on the BraTS-Lighthouse SSA 2025 benchmark.
MBZUAI researchers introduce UniMed-CLIP, a unified Vision-Language Model (VLM) for diverse medical imaging modalities, trained on the new large-scale, open-source UniMed dataset. UniMed comprises over 5.3 million image-text pairs across six modalities: X-ray, CT, MRI, Ultrasound, Pathology, and Fundus, created using LLMs to transform classification datasets into image-text formats. UniMed-CLIP significantly outperforms existing generalist VLMs and matches modality-specific medical VLMs in zero-shot evaluations, improving over BiomedCLIP by +12.61 on average across 21 datasets while using 3x less training data.
Researchers propose a universal anatomical embedding (UAE) framework for medical image analysis to learn appearance, semantic, and cross-modality anatomical embeddings. UAE incorporates semantic embedding learning with prototypical contrastive loss, a fixed-point-based matching strategy, and an iterative approach for cross-modality embedding learning. The framework was evaluated on landmark detection, lesion tracking and CT-MRI registration tasks, outperforming existing state-of-the-art methods.
MBZUAI has launched The Academy, a global platform for AI knowledge exchange, along with the world’s first AI x Arts Fellowship. The inaugural cohort of eight fellows includes artists and innovators like Refik Anadol and Amrita Sethi, who will participate in residency programs in Abu Dhabi in 2026. The Academy also encompasses the MBZUAI Executive Program (MEP), which empowers UAE leaders to leverage AI. Why it matters: This initiative signals MBZUAI's commitment to fostering interdisciplinary collaboration between AI and the arts, positioning Abu Dhabi as a hub for innovative AI applications in culture and heritage.
MBZUAI researchers developed ClinGRAD, a multimodal graph neural network that analyzes genomic data, MRI scans, and clinical information to classify dementia types (Alzheimer's, vascular, etc.). The system addresses the challenge of high misdiagnosis rates (up to 30%) in dementia, where incorrect diagnoses can significantly impact patient life expectancy. ClinGRAD aims to be an interpretable AI system, providing explainability to clinicians. Why it matters: Accurate and early diagnosis of dementia subtypes is crucial for slowing disease progression and improving patient care in the region, where the prevalence of dementia is expected to rise significantly.
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.
Eleven alumni from KAUST's Saudi Research Science Institute (SRSI) excelled at the Regeneron International Science and Engineering Fair (ISEF), a global pre-college STEM competition. The Saudi team, including SRSI participants, collectively secured 12 grand prizes and 12 special awards at the event held in Phoenix, Arizona. Four SRSI alumni received high recognition, including Lana Alaggad for her project on optoelectronic memristors for neuromorphic vision systems. Why it matters: This highlights Saudi Arabia's investment in cultivating young STEM talent through programs like SRSI at KAUST, preparing future innovators and aligning with Saudi Vision 2030's human capital development goals, with specific relevance to AI in neuromorphic computing.
This paper introduces BRIQA, a new method for automated assessment of artifact severity in pediatric brain MRI, which is important for diagnostic accuracy. BRIQA uses gradient-based loss reweighting and a rotating batching scheme to handle class imbalance in artifact severity levels. Experiments show BRIQA improves average macro F1 score from 0.659 to 0.706, especially for Noise, Zipper, Positioning and Contrast artifacts.