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…
GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models
Favour Nerrise, Lucy Yin, Mohammad H. Abbasi +2
Brain MRI foundation models learn rich representations of anatomy, but interpreting what clinical information they encode remains an open problem. Standard sparse autoencoders (SAE…
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…
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…
Confounder-Free Continual Learning via Recursive Feature Normalization
Yash Shah, Camila Gonzalez, Mohammad H. Abbasi +3
Confounders are extraneous variables that affect both the input and the target, resulting in spurious correlations and biased predictions. There are recent advances in dealing with…
Generating Novel Brain Morphology by Deforming Learned Templates
Alan Q. Wang, Fangrui Huang, Bailey Trang +5
Designing generative models for 3D structural brain MRI that synthesize morphologically-plausible and attribute-specific (e.g., age, sex, disease state) samples is an active area o…