Da-Woon Heo

HyFI: Hyperbolic Feature Interpolation for Brain-Vision Alignment

Sangmin Jo, Wootaek Jeong, Da-Woon Heo, Yoohwan Hwang et autres

Recent progress in artificial intelligence has encouraged numerous attempts to understand and decode human visual system from brain signals. These prior works typically align neural activity independently with semantic and perceptual features extracted from images using pre-trained vision models. However, they fail …

HyFI: Hyperbolic Feature Interpolation for Brain-Vision Alignment

Sang‐min Jo, Wootaek Jeong, Da-Woon Heo, Yoohwan Hwang et autres

Recent progress in artificial intelligence has encouraged numerous attempts to understand and decode human visual system from brain signals. These prior works typically align neural activity independently with semantic and perceptual features extracted from images using pre-trained vision models. However, they fail …

Transferring ultrahigh-field representations for intensity-guided brain segmentation of low-field magnetic resonance imaging

Kwanseok Oh, Jieun Lee, Da-Woon Heo, Dinggang Shen et autres

Ultrahigh-field (UHF) magnetic resonance imaging (MRI), 7T MRI, provides superior anatomical details of internal brain structures thanks to its enhanced signal-to-noise ratio and susceptibility-induced contrast. However, the widespread use of 7T MRI is limited by its high cost and lower accessibility compared …

kr, cn (code pays fourni par la source)

Complex wavelet-based Transformer for neurodevelopmental disorder diagnosis via direct modeling of real and imaginary components

Ah-Yeong Jeong, Da-Woon Heo, Heung‐Il Suk

Resting-state functional magnetic resonance imaging (rs-fMRI) measures intrinsic neural activity, and analyzing its frequency-domain characteristics provides insights into brain dynamics. Owing to these properties, rs-fMRI is widely used to investigate brain disorders such as autism spectrum disorder (ASD) and attention deficit hyperactivity …

kr, jp (code pays fourni par la source)

Explainable Normative Modeling for Brain Disorder Identification in Resting-State fMRI

Yeajin Shon, Eunsong Kang, Da-Woon Heo, Heung‐Il Suk

Accurate identification of brain disorders enables timely intervention and improved patient outcomes. While numerous studies have developed AI models for resting-state functional magnetic resonance imaging (rs-fMRI) analysis, most rely on supervised learning, which can overlook hidden patterns that are less discriminatively associated …

kr (code pays fourni par la source)

FIESTA: Fourier-Based Semantic Augmentation With Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation

Kwanseok Oh, Eunjin Jeon, Da-Woon Heo, Yooseung Shin et autres

Single-source domain generalization (SDG) in medical image segmentation (MIS) aims to generalize a model using only one source domain data to segment data from an unseen target domain. Despite substantial advances in SDG with data augmentation, existing methods often fail to fully …

kr (code pays fourni par la source)

A quantitatively interpretable model for Alzheimer’s disease prediction using deep counterfactuals

Kwanseok Oh, Da-Woon Heo, Ahmad Wisnu Mulyadi, Wonsik Jung et autres

Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disease progression yet still demands attentive interpretability to explain how their DL models make definitive decisions. Counterfactual reasoning has recently gained increasing attention in medical research because of its …

kr (code pays fourni par la source)

FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation

Kwanseok Oh, Eunjin Jeon, Da-Woon Heo, Yooseung Shin et autres

Single-source domain generalization (SDG) in medical image segmentation (MIS) aims to generalize a model using data from only one source domain to segment data from an unseen target domain. Despite substantial advances in SDG with data augmentation, existing methods often fail to …

Transferring Ultrahigh-Field Representations for Intensity-Guided Brain Segmentation of Low-Field Magnetic Resonance Imaging

Kwanseok Oh, Jieun Lee, Da-Woon Heo, Dinggang G. Shen et autres

Ultrahigh-field (UHF) magnetic resonance imaging (MRI), i.e., 7T MRI, provides superior anatomical details of internal brain structures owing to its enhanced signal-to-noise ratio and susceptibility-induced contrast. However, the widespread use of 7T MRI is limited by its high cost and lower accessibility …

BrainWaveNet: Wavelet-Based Transformer for Autism Spectrum Disorder Diagnosis

Ah-Yeong Jeong, Da-Woon Heo, Eunsong Kang, Heung‐Il Suk

kr (code pays fourni par la source)

A Learnable Counter-Condition Analysis Framework for Functional Connectivity-Based Neurological Disorder Diagnosis

Eunsong Kang, Da-Woon Heo, Ji‐Won Lee, Heung‐Il Suk

To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, …

kr (code pays fourni par la source)

Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders

Woo‐Sung Kim, Da-Woon Heo, Junyeong Maeng, Jie Shen et autres

BACKGROUND AND HYPOTHESIS: The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PAD in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging (sMRI). STUDY DESIGN: We …

kr, cn, us (code pays fourni par la source)