MBZUAI faculty, researchers, and students presented eight academic papers at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022) in Singapore. Seven of the accepted papers feature a master’s or doctoral student as first author. The papers are the outcome of two MBZUAI faculty led labs – BioMedical Image Analysis (BioMedIA) lab and SPriNT-AI. Why it matters: This highlights MBZUAI's growing prominence in medical image analysis and AI, showcasing the university's commitment to producing high-quality research and fostering young talent in the field.
MBZUAI students won the top three awards at the Alibaba AI Hackathon in Dubai, part of GITEX Global AI InnovateFest. First place went to TalkTrain, a smart business assistant for public speaking practice, developed by Kane Lindsay and Hawau Olamide Toyin. Second place was awarded to Anaaya, a generative AI application for predicting adverse drug interactions, created by Mai A. Shaaban and Anees Ur Rehman Hashmi. Why it matters: This sweep highlights MBZUAI's strength in applied AI research and the potential for its students to create impactful solutions for real-world problems.
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.
A team of MBZUAI graduate students won first place at the UAE University's University Challenge for their SawabAI project, which addresses AI-generated misinformation about climate change. The winning team included Salem Bin Saqer AlMarri, Hanoona Bangalath, and Muhammad Maaz, all Computer Vision Ph.D. students. SawabAI is envisioned as a platform to evaluate the authenticity and source of information, including text, image, and video, to combat fake news. Why it matters: This win highlights the growing importance of AI in addressing misinformation and promoting sustainability in government communication within the region.
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.