7 citations · 11 across the 8 of their papers we have counts for
8 papers
Batch Effects In Brain Foundation Model Embeddings
Ye Tao, Bradley T. Baker, Yu Wu +4
Foundation models show strong potential for large-scale, high-dimensional biomedical applications, yet their ability to capture relevant neurobiological characteristics remains und…
Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Help
Keqi Han, Yao Su, Lifang He +4
Graph deep learning models, a class of AI-driven approaches employing a message aggregation mechanism, have gained popularity for analyzing the functional brain connectome in neuro…
Copula-Linked Parallel ICA: A Method for Coupling Structural and Functional MRI brain Networks
Oktay Agcaoglu, Rogers F. Silva, Deniz Alacam +3
Different brain imaging modalities offer unique insights into brain function and structure. Combining them enhances our understanding of neural mechanisms. Prior multimodal studies…
SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning
Riyasat Ohib, Bishal Thapaliya, Gintare Karolina Dziugaite +3
In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subne…
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…
SalientGrads: Sparse Models for Communication Efficient and Data Aware Distributed Federated Training
Riyasat Ohib, Bishal Thapaliya, Pratyush Gaggenapalli +3
Federated learning (FL) enables the training of a model leveraging decentralized data in client sites while preserving privacy by not collecting data. However, one of the significa…
Self-Supervised Mental Disorder Classifiers via Time Reversal
Zafar Iqbal, Usman Mahmood, Zening Fu +1
Data scarcity is a notable problem, especially in the medical domain, due to patient data laws. Therefore, efficient Pre-Training techniques could help in combating this problem. I…
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…