10 citations · 24 across the 14 of their papers we have counts for
16 papers
XMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence
Sepehr Salem Ghahfarokhi, M. Moein Esfahani, Raj Sunderraman +2
Deep learning has significantly advanced automated brain tumor diagnosis, yet clinical adoption remains limited by interpretability and computational constraints. Conventional mode…
Generative forecasting of brain activity enhances Alzheimer's classification and interpretation
Yutong Gao, Vince D. Calhoun, Robyn L. Miller
Understanding the relationship between cognition and intrinsic brain activity through purely data-driven approaches remains a significant challenge in neuroscience. Resting-state f…
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis
Yutong Gao, Charles A. Ellis, Vince D. Calhoun +1
The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high-performing deep learning models. However, the neuroimaging field i…
Predictive Sparse Manifold Transform
Yujia Xie, Xinhui Li, Vince D. Calhoun
We present Predictive Sparse Manifold Transform (PSMT), a minimalistic, interpretable and biologically plausible framework for learning and predicting natural dynamics. PSMT incorp…
Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey
Md. Mahfuzur Rahman, Vince D. Calhoun, Sergey M. Plis
Deep learning (DL) models have been popular due to their ability to learn directly from the raw data in an end-to-end paradigm, alleviating the concern of a separate error-prone fe…
Learning low-dimensional dynamics from whole-brain data improves task capture
Eloy Geenjaar, Donghyun Kim, Riyasat Ohib +4
The neural dynamics underlying brain activity are critical to understanding cognitive processes and mental disorders. However, current voxel-based whole-brain dimensionality reduct…
Higher-order Organization in the Human Brain from Matrix-Based Rényi's Entropy
Qiang Li, Shujian Yu, Kristoffer H Madsen +2
Pairwise metrics are often employed to estimate statistical dependencies between brain regions, however they do not capture higher-order information interactions. It is critical to…
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…