6 papers
NeuroQA: A Large-Scale Image-Grounded Benchmark for 3D Brain MRI Understanding
Mohammad H. Abbasi, Favour Nerrise, Shaurnav Ghosh +12
We present NeuroQA, a large-scale benchmark for visual question answering in 3D brain magnetic resonance imaging (MRI), with 56,953 QA pairs from 12,977 subjects across 12 datasets…
MIRAGE: The Illusion of Visual Understanding
Mohammad Asadi, Jack W. O'Sullivan, Fang Cao +5
Multimodal AI systems have achieved remarkable performance across a broad range of real-world tasks, yet the mechanisms underlying visual-language reasoning remain surprisingly poo…
Diffusion MRI Transformer with a Diffusion Space Rotary Positional Embedding (D-RoPE)
Gustavo Chau Loo Kung, Mohammad Abbasi, Camila Blank +6
Diffusion Magnetic Resonance Imaging (dMRI) plays a critical role in studying microstructural changes in the brain. It is, therefore, widely used in clinical practice; yet progress…
MARCUS: An agentic, multimodal vision-language model for cardiac diagnosis and management
Jack W O'Sullivan, Mohammad Asadi, Lennart Elbe +8
Cardiovascular disease remains the leading cause of global mortality, with progress hindered by human interpretation of complex cardiac tests. Current AI vision-language models are…
Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain
Mohammad Asadi, Tahoura Nedaee, Jack W. O'Sullivan +2
Multimodal large language models (MLLMs) have shown strong potential for medical Visual Question Answering (VQA), yet they remain prone to hallucinations, defined as generating res…
Interpretable Cross-Network Attention for Resting-State fMRI Representation Learning
Karanpartap Singh, Adam Turnbull, Mohammad Abbasi +3
Understanding how large-scale functional brain networks reorganize during cognitive decline remains a central challenge in neuroimaging. While recent self-supervised models have sh…