10 citations · 24 across the 14 of their papers we have counts for
9 papers · 1 filter
New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning
Fateme Ghayem, Hanlu Yang, Furkan Kantar +3
Independent component analysis (ICA) of multi-subject functional magnetic resonance imaging (fMRI) data has proven useful in providing a fully multivariate summary that can be used…
Coupled CP tensor decomposition with shared and distinct components for multi-task fMRI data fusion
Ricardo Augusto Borsoi, Isabell Lehmann, Mohammad Abu Baker Siddique Akhonda +4
Discovering components that are shared in multiple datasets, next to dataset-specific features, has great potential for studying the relationships between different subjects or tas…
MultiCrossViT: Multimodal Vision Transformer for Schizophrenia Prediction using Structural MRI and Functional Network Connectivity Data
Yuda Bi, Anees Abrol, Zening Fu +1
Vision Transformer (ViT) is a pioneering deep learning framework that can address real-world computer vision issues, such as image classification and object recognition. Importantl…
CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs
Eloy Geenjaar, Noah Lewis, Amrit Kashyap +2
Communication within or between complex systems is commonplace in the natural sciences and fields such as graph neural networks. The brain is a perfect example of such a complex sy…
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes
Alex Fedorov, Eloy Geenjaar, Lei Wu +7
Recent neuroimaging studies that focus on predicting brain disorders via modern machine learning approaches commonly include a single modality and rely on supervised over-parameter…
Pipeline-Invariant Representation Learning for Neuroimaging
Xinhui Li, Alex Fedorov, Mrinal Mathur +4
Deep learning has been widely applied in neuroimaging, including predicting brain-phenotype relationships from magnetic resonance imaging (MRI) volumes. MRI data usually requires e…
Explainable AI (XAI) in Biomedical Signal and Image Processing: Promises and Challenges
Guang Yang, Arvind Rao, Christine Fernandez-Maloigne +2
Artificial intelligence has become pervasive across disciplines and fields, and biomedical image and signal processing is no exception. The growing and widespread interest on the t…
Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data
Eloy Geenjaar, Amrit Kashyap, Noah Lewis +2
Functional magnetic resonance imaging (fMRI) data contain complex spatiotemporal dynamics, thus researchers have developed approaches that reduce the dimensionality of the signal w…
Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest
Moo K. Chung, Shih-Gu Huang, Ian C. Carroll +2
We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. O…