5 citations · 8 across the 10 of their papers we have counts for
12 papers
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation
Protyay Dey, Ayush Roy, Hyuna Cho +2
Functional brain connectome represents neural connectivity as a matrix of pairwise interactions between brain regions. Generation of functional connectomes is not only a question o…
MnemoDyn: Learning Resting State Dynamics from 40K FMRI sequences
Sourav Pal, Viet Luong, Hoseok Lee +5
We present a dynamical-systems based model for resting-state functional magnetic resonance imaging (rs-fMRI), trained on a dataset of roughly 40K rs-fMRI sequences covering a wide…
ENC-ODE: Event-level Neurodegenerative Modeling in Continuous Time with Neural ODEs
Yujee Song, Seunghun Baek, Guorong Wu +1
Accurately predicting the temporal evolution of clinical biomarkers is crucial for the early diagnosis and management of neurodegenerative diseases such as Alzheimer's disease. How…
Set-Inclusive Uncertainty Modeling for Robust Brain Tumor Segmentation
Seunghun Baek, Jihwan Park, Jaeyoon Sim +3
Multimodal MRI is essential for accurate brain tumor segmentation. However, acquiring all modalities at inference is often challenging in practice, which causes intrinsic uncertain…
Multi-Modal Graph Neural Network with Transformer-Guided Adaptive Diffusion for Preclinical Alzheimer Classification
Jaeyoon Sim, Minjae Lee, Guorong Wu +1
The graphical representation of the brain offers critical insights into diagnosing and prognosing neurodegenerative disease via relationships between regions of interest (ROIs). De…
Learning Multi-Scale Hypergraph for High-Order Brain Connectivity Analysis
Jaeyoon Sim, Soojin Hwang, Seunghun Baek +2
Understanding complex interactions between brain regions is critical for early neurodegenerative disease classification such as Alzheimer's Disease (AD) and Parkinson's Disease (PD…
Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?
Ayush Roy, Samin Enam, Jun Xia +2
Data scarcity is a major challenge in medical imaging, particularly for deep learning models. While data pooling (combining datasets from multiple sources) and data addition (addin…
OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels
Seunghun Baek, Jaeyoon Sim, Guorong Wu +1
Accurately discriminating progressive stages of Alzheimer's Disease (AD) is crucial for early diagnosis and prevention. It often involves multiple imaging modalities to understand…
Modality-Agnostic Style Transfer for Holistic Feature Imputation
Seunghun Baek, Jaeyoon Sim, Mustafa Dere +3
Characterizing a preclinical stage of Alzheimer's Disease (AD) via single imaging is difficult as its early symptoms are quite subtle. Therefore, many neuroimaging studies are cura…
Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations
Jiaqi Ding, Tingting Dan, Ziquan Wei +4
An unprecedented amount of existing functional Magnetic Resonance Imaging (fMRI) data provides a new opportunity to understand the relationship between functional fluctuation and h…
Exploring the Enigma of Neural Dynamics Through A Scattering-Transform Mixer Landscape for Riemannian Manifold
Tingting Dan, Ziquan Wei, Won Hwa Kim +1
The human brain is a complex inter-wired system that emerges spontaneous functional fluctuations. In spite of tremendous success in the experimental neuroscience field, a system-le…
Learning to Approximate Adaptive Kernel Convolution on Graphs
Jaeyoon Sim, Sooyeon Jeon, InJun Choi +2
Various Graph Neural Networks (GNNs) have been successful in analyzing data in non-Euclidean spaces, however, they have limitations such as oversmoothing, i.e., information becomes…