38 citations · 57 across the 13 of their papers we have counts for
15 papers
Do Vision Models Learn Physical Constraints or Rendering Shortcuts? A Counterfactual Benchmark for Grounded Physical Consistency
M. Moein Esfahani, Sepehr Salem, Mohammed Alser +1
Modern image editing models can satisfy a text instruction while breaking the physics of the edited scene. A new object may cast no shadow, a mirror may fail to reflect visible geo…
Lost in Distortion: Uncovering the Domain Gap Between Computer Vision and Brain Imaging -- A Study on Pretraining for Age Prediction
Yanteng Zhang, Songheng Li, Zeyu Shen +4
Large-scale brain imaging datasets provide unprecedented opportunities for developing domain foundation models through pretraining. However, unlike natural image datasets in comput…
Breast Cancer Detection in Thermographic Images via Diffusion-Based Augmentation and Nonlinear Feature Fusion
Sepehr Salem, M. Moein Esfahani, Jingyu Liu +1
Data scarcity hinders deep learning for medical imaging. We propose a framework for breast cancer classification in thermograms that addresses this using a Diffusion Probabilistic…
Mapping minds not averages: a scalable subject-specific manifold learning framework for neuroimaging data
Eloy Geenjaar, Vince Calhoun
Mental and cognitive representations are believed to reside on low-dimensional, non-linear manifolds embedded within high-dimensional brain activity. Uncovering these manifolds is…
Go Figure: Transparency in neuroscience images preserves context and clarifies interpretation
Paul A. Taylor, Himanshu Aggarwal, Peter Bandettini +39
Visualizations are vital for communicating scientific results. Historically, neuroimaging figures have only depicted regions that surpass a given statistical threshold. This practi…
Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity
Bishal Thapaliya, Esra Akbas, Ram Sapkota +3
Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the r…
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…
Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images
Nagur Shareef Shaik, Teja Krishna Cherukuri, Vince Calhoun +1
Schizophrenia is a debilitating, chronic mental disorder that significantly impacts an individual's cognitive abilities, behavior, and social interactions. It is characterized by s…
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…
Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data
Bishal Thapaliya, Esra Akbas, Jiayu Chen +5
Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for t…
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…
Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series
Usman Mahmood, Zening Fu, Vince Calhoun +1
Recently, methods that represent data as a graph, such as graph neural networks (GNNs) have been successfully used to learn data representations and structures to solve classificat…
Multi network InfoMax: A pre-training method involving graph convolutional networks
Usman Mahmood, Zening Fu, Vince Calhoun +1
Discovering distinct features and their relations from data can help us uncover valuable knowledge crucial for various tasks, e.g., classification. In neuroimaging, these features…
Peering Beyond the Gradient Veil with Distributed Auto Differentiation
Bradley T. Baker, Aashis Khanal, Vince D. Calhoun +2
Although distributed machine learning has opened up many new and exciting research frontiers, fragmentation of models and data across different machines, nodes, and sites still res…
Iterative Refinement of the Approximate Posterior for Directed Belief Networks
R Devon Hjelm, Kyunghyun Cho, Junyoung Chung +3
Variational methods that rely on a recognition network to approximate the posterior of directed graphical models offer better inference and learning than previous methods. Recent a…