91 citations · 115 across the 11 of their papers we have counts for
11 papers
MEASURE: Multi-scale Minimal Sufficient Representation Learning for Domain Generalization in Sleep Staging
Sangmin Jo, Jee Seok Yoon, Wootaek Jeong +2
Deep learning-based automatic sleep staging has significantly advanced in performance and plays a crucial role in the diagnosis of sleep disorders. However, those models often stru…
IdenBAT: Disentangled Representation Learning for Identity-Preserved Brain Age Transformation
Junyeong Maeng, Kwanseok Oh, Wonsik Jung +1
Brain age transformation aims to convert reference brain images into synthesized images that accurately reflect the age-specific features of a target age group. The primary objecti…
DyMix: Dynamic Frequency Mixup Scheduler based Unsupervised Domain Adaptation for Enhancing Alzheimer's Disease Identification
Yooseung Shin, Kwanseok Oh, Heung-Il Suk
Advances in deep learning (DL)-based models for brain image analysis have significantly enhanced the accuracy of Alzheimer's disease (AD) diagnosis, allowing for more timely interv…
FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation
Kwanseok Oh, Eunjin Jeon, Da-Woon Heo +2
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 targe…
Transferring Ultrahigh-Field Representations for Intensity-Guided Brain Segmentation of Low-Field Magnetic Resonance Imaging
Kwanseok Oh, Jieun Lee, Da-Woon Heo +2
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…
The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography
Kaiyuan Yang, Fabio Musio, Yihui Ma +112
The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to influence the risk, severity, an…
A Quantitatively Interpretable Model for Alzheimer's Disease Prediction Using Deep Counterfactuals
Kwanseok Oh, Da-Woon Heo, Ahmad Wisnu Mulyadi +4
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…
Domain Generalization for Medical Image Analysis: A Review
Jee Seok Yoon, Kwanseok Oh, Yooseung Shin +2
Medical image analysis (MedIA) has become an essential tool in medicine and healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in deep…
XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning
Ahmad Wisnu Mulyadi, Wonsik Jung, Kwanseok Oh +2
Diagnosing Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristic…
Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Model
Kwanseok Oh, Jee Seok Yoon, Heung-Il Suk
Existing studies on disease diagnostic models focus either on diagnostic model learning for performance improvement or on the visual explanation of a trained diagnostic model. We p…
Born Identity Network: Multi-way Counterfactual Map Generation to Explain a Classifier's Decision
Kwanseok Oh, Jee Seok Yoon, Heung-Il Suk
There exists an apparent negative correlation between performance and interpretability of deep learning models. In an effort to reduce this negative correlation, we propose a Born…