5 citations · 7 across the 6 of their papers we have counts for
8 papers
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…
Residual-Guided Expert Specialization for Incomplete Multimodal Learning
Seunghun Baek, Jihwan Park, Jaeyoon Sim +3
As real-world prediction systems often face missing modalities at inference, incomplete multimodal learning (IML) remains a practical challenge. While prior methods aim to learn re…
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…
MNM : Multi-level Neuroimaging Meta-analysis with Hyperbolic Brain-Text Representations
Seunghun Baek, Jaejin Lee, Jaeyoon Sim +2
Various neuroimaging studies suffer from small sample size problem which often limit their reliability. Meta-analysis addresses this challenge by aggregating findings from differen…
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…
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…