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.
MBZUAI researchers are introducing MedNNS, a system to be presented at MICCAI 2025, that recommends the best AI architecture and initialization for medical imaging tasks. MedNNS addresses the challenge of inefficient trial-and-error in building medical imaging AI by reframing model selection as a retrieval problem. The system employs a Once-For-All ResNet-like model and a learned meta-space of 720k model-dataset pairs, using dataset embeddings to predict optimal model performance. Why it matters: By automating model selection, MedNNS promises to significantly reduce the time and resources required to develop effective AI solutions for healthcare, particularly in medical imaging.
MBZUAI researchers developed a new approach called Multimodal Optimal Transport via Grounded Retrieval (MOTOR) to improve the accuracy of vision-language models for medical image analysis. MOTOR combines retrieval-augmented generation (RAG) with an optimal transport algorithm to retrieve and rank relevant image and textual data. Testing on two medical datasets showed that MOTOR improved average performance by 6.45%. Why it matters: This technique addresses the challenges of limited specialized medical datasets and computational costs associated with training AI models for medical image interpretation, offering a more efficient and accurate solution.
MBZUAI researchers developed a new deep learning method for rapid and accurate estimation of clinical measurements from echocardiograms. The method focuses on improving the measurement of the left ventricle ejection fraction, a key indicator of heart health. Their deep learning approach improves upon previous methods by better organizing data representation, enhancing performance and transferability. Why it matters: The AI-driven solution can potentially reduce analysis time for cardiologists, improve patient care, and be particularly beneficial in regions with limited healthcare resources.
MBZUAI researchers developed a method to adapt Meta's Segment Anything Model (SAM) for medical image segmentation, addressing its performance gap with natural images. Their approach improves SAM's accuracy without requiring extensive retraining or large medical image datasets. The research, led by Chao Qin, was nominated for the Best Paper Award at the MICCAI conference in Marrakesh. Why it matters: This offers a more efficient and effective way to leverage foundation models in specialized medical imaging applications, potentially improving diagnostic accuracy and reducing the need for large-scale, domain-specific training data.
Faris Almalik, an MBZUAI alumnus, discusses his journey from mechanical engineering to becoming a senior data scientist at the Department of Government Enablement – TAMM in Abu Dhabi. Almalik emphasizes his passion for continuous learning and knowledge sharing, which has driven his career across defense, education, finance, and government sectors. He published his first paper on adversarial attacks on medical imaging at MICCAI during his time at MBZUAI. Why it matters: This highlights the success of MBZUAI in producing impactful AI professionals who contribute to both research and practical applications in key sectors within the UAE.
MBZUAI researchers developed Human-in-the-Loop for Prognosis (HuLP), a new AI system designed to help physicians assess cancer progression by providing information about its predictions and allowing user intervention. The system aims to foster collaboration between physicians and AI, rather than replacing doctors. It was presented at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Why it matters: This research highlights the potential of AI to augment physician expertise in critical areas like cancer prognosis, improving patient care and treatment decisions.
Researchers at MBZUAI have developed a new machine learning method called survival rank-n-contrast (SurvRNC) to improve survival models for cancer prognoses. The method is designed to predict survival times for head and neck cancer patients using multimodal data while accounting for censored data (missing values). Numan Saeed presented the team’s work at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Why it matters: Accurate prognoses can significantly improve patient outcomes, and this research contributes to advancements in machine learning techniques for handling complex and incomplete medical data.