Publications

(Co-work with Sungshin Univ.) A Novel Data-Oriented Method of Self-Supervision for Neurodegenerative Brain Disease Diagnosis using EEG Signals, Under review

(Co-work with Sungshin Univ.) A Data-Centric Self-Supervised Learning for Phenotype–Genotype Relation Modeling via Deep Graph Neural Network Architecture, Medical Image Analysis, Under review.

(Co-work with Korea Univ.) W. Park, W. Jung+, and H.-I. Suk+, “ProtoCare+: Knowledge Graph Guided Representation Learning for Diagnosis Prediction,” ACM Transactions on Computing for Healthcare, Under review. (+: co-corresponding)

W. Jung, "Leveraging Multi-View Contrastive Learning for Brain Disorder Diagnosis and Functional Connectivity Modeling," in preparation.

C. Lee, ... and W. Jung, "Diffusion-Inspired Ultrasound Restoration without Synthetic Noise Modeling," Under review. (corresponding author)

M. Jeong+, C. Park+, and W. Jung*, "K-MoDE: A Two-Stage Parameter-Efficient Fine-Tuning Framework for Dialect-Preserving Korean Multi-Dialect ASR," Under review. (+: equal contribution, *: corresponding author)

S. Lee and W. Jung, "A Study on Optimization and Growth Uncertainty Analysis of Zanthoxylum piperitum under Shading Environments in Open-Field Cultivation Using Bayesian Neural Networks," Under review.

(Patent) 2-Hydroxycinnamaldehyde를 유효성분으로 함유하는 퇴행성 뇌 질환의 예방 개선 또는 치료용 조성물, 출원번호: 10-2026-0008653, 2026.01.16.

(Patent) 석흥일, 정원식, “뇌 질환 예측 장치 및 방법, 뇌 질환을 예측하기 위한 학습 장치”, 등록번호: 10-2605720, 등록일자: 2023.11.20

(Patent) 석흥일, 정원식, “뇌 질환 예측 장치 및 방법, 뇌 질환을 예측하기 위한 학습 장치”, 국제 출원 번호 : 제 PCT/KR2020/003131호, 출원일자: 2020.03.06

[J10] J. Maeng, K. Oh, W. Jung, and H.-I. Suk, “IdenBAT: Disentangled Representation Learning for Identity-Preserved Brain Age Transformation,” Artificial Intelligence In Medicine, 2025 (JCR-IF: 6.1) [paper].

[J9] K. Oh, D.-W. Heo, A.W. Mulyadi, W. Jung, E. Kang, K.H. Lee, and H.-I. Suk, “A Quantitatively Interpretable Model for Alzheimer's Disease Prediction using Deep Counterfactuals,” NeuroImage, 2025. (JCR-IF: 4.7) [paper], [code]

[J8] S. Jeong*, W. Jung*, J. Sohn, and H.-I. Suk, “Deep Geometric Learning with Monotonicity Constraints for Alzheimer's Disease Progression,” IEEE Transactions on Neural Networks and Learning Systems, Accepted, 2024. (JCR-IF: 14.255) [paper], [code] (*: equally contributed)

[J7] W. Jung*, S.E. Kim*, J.P. Kim, H. Jang, C.J. Park, H.J. Kim, D.L. NA, S.W. Seo†, and H.-I. Suk†, “Deep learning model for individualized trajectory prediction of clinical outcomes in mild cognitive impairment,” Frontiers in Aging Neuroscience, Accepted, 2024. (JCR-IF: 4.8), [paper] (*: equal contribution, †: equal correspondence)

[J6] W. Jung, E. Jeon, E. Kang, and H.-I. Suk, “EAG-RS: A Novel Explainability-guided ROI Selection Framework for ASD diagnosis via Inter-regional Relation Learning,” IEEE Transactions on Medical Imaging, 2023. (JCR-IF: 11.037), [paper], [code]

[J5] J. Sohn, E. Jeon, W. Jung, E. Kang, and H.-I. Suk, “Module of Axis-based Nexus Attention for Weakly Supervised Object Localization,” Scientific Reports, 2023. (JCR-IF: 4.997), [paper], [code]

[J4] C. Park*, W. Jung*, and H.-I. Suk, “Deep Joint Learning of Pathological Region Localization and Alzheimer's Disease Diagnosis,” Scientific Reports, 2023. (JCR-IF: 4.997) [paper], [code] (*: equally contributed)

[J3] A.W. Mulyadi, W. Jung, K. Oh, J.S. Yoon, and H.-I. Suk, “Estimating Explainable Alzheimer’s Disease Likelihood Map via Clinically-guided Prototype Learning,” NeuroImage, 2023. (JCR-IF: 7.400) [paper], [code]

[J2] W. Ko, W. Jung, E. Jeon, and H.-I. Suk, “A Deep Generative–Discriminative Learning for Multi-modal Representation in Imaging Genetics,” IEEE Transactions on Medical Imaging, 2022. (JCR-IF: 10.048) [paper], [code]

[J1] W. Jung, E. Jun, and H.-I. Suk, “Deep Recurrent Model for Individualized Prediction of Alzheimer’s Disease Progression,” NeuroImage, 2021. (JCR-IF: 6.556) [paper], [code]

[C9] M. Jeong+, J. Yu+, and W. Jung*, "Zero-Shot Generalization Enables Cross-Release EEG Response-Time Decoding," Organization for Human Brain Mapping (OHBM), Accepted, 2026 (+: equal contribution, *: corresponding author)

[C8] C. Park, M. Cho, Y. Kim, W. Jung, and W. Ko, "A Novel Self-Supervised Deep Learning Framework for Pattern Recognition of Neurodegenerative Disease via Graph-based Phenotype–Genotype Relation Modeling," the 41st ACM/SIGAPP Symposium on Applied Computing (SAC), Accepted, 2026. (Acceptance rate: 24%)

[C7] W. Jung and H.-I. Suk, “Enhanced Functional-Connectivity Representation by Contrastive Learning for Brain Disease Diagnosis,” Organization for Human Brain Mapping (OHBM), Accepted, 2024.

[C6] K. Oh, D.-W. Heo, A.W. Mulyadi, W. Jung, E. Kang, and H.-I. Suk, “Quantifying Explainability of Counterfactual-Guided MRI Feature for Alzheimer’s Disease Prediction,” 2022 NeurIPS Workshop: Medical Imaging meets NeurIPS (MedNeurIPS), Accepted, 2022.

[C5] A.W. Mulyadi, W. Jung, K. Oh, J.S. Yoon, and H.-I. Suk, “Clinically-guided Prototype Learning and Its Use for Explanation in Alzheimer's Disease Identification,” 2022 NeurIPS Workshop: Medical Imaging meets NeurIPS (MedNeurIPS), Accepted, 2022. (Oral)

[C4] S. Jeong, W. Jung, J. Sohn, and H.-I. Suk, “Deep Geometrical Learning for Alzheimer's Disease Progression Modeling,” IEEE International Conference on Data Mining (ICDM), pp. 211-220, 2022. (Acceptance rate: 9.77%)

[C3] W. Ko, W. Jung, E. Jeon, A.W. Mulyadi, and H.-I. Suk, “ENGINE: Enhancing Neuroimaging and Genetic Information by Neural Embedding,” IEEE International Conference on Data Mining (ICDM), pp. 1162-1167, 2021. (Acceptance rate: 20%)

[C2] W. Jung, D.-W. Heo, E. Jeon, J. Lee, and H.-I. Suk, “Inter-Regional High-level Relation Learning of Functional Connectivity via Self-Supervision,” Proc. of 2021 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 284-293, Springer, 2021. (Poster)

[C1] W. Jung, A.W. Mulyadi, and H.-I. Suk, “Unified Modeling of Imputation, Forecasting, and Prediction for AD Progression,” Proc. of 2019 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 168–176, Springer, 2019. (Early accept, Poster)

[D27] J. Yu+, M. Jeong+, C. Ahn, W. Ko, and W. Jung, "Bidirectional Cross-Attention-based Multimodal Learning Model for Alzheimer’s Disease Stage Classification Using Genetic and Neuroimaging Data," Proc. of 2026 KIISE Korea Computer Congress (KCC), 2026. (+: equal contribution)

[D26] A. Jeong+, J. Seo+, K. Jang, and W. Jung, "Post-hoc Logit Refinement via Contrastive Top-K Guidance for Zero-shot Open Vocabulary Segmentation," Proc. of 2026 KIISE Korea Computer Congress (KCC), 2026. (+: equal contribution)

[D25] S.-H. Kim+, J.-H. Kim+, and W. Jung, "A Study on Frequency Integration Strategies with Swin Transformer for AI-generated Image Detection", The Journal of the Korea Institute of Electronic Communication Sciences, Accepted, April, 2026. (+: equal contribution)

[D24] W. Jung* and D. Ko, "Tokenization and Pretraining Strategies in Genomic Language Models: A Representation Learning Perspective," The Journal of the Korea institute of electronic communication sciences, Accpeted, January, 2026. (*: corresponding author)

[D23] 정아영, 정원식, "저조도 영상 보정 기법에 따른 YOLOv11 기반 객체 탐지 성능 비교 분석에 관한 연구", 한국인공지능융합기술학회 추계학술대회, 2025.

[D22] 장근혁*, 서재석*, 정원식, "의료 보고서 생성 성능 향상을 위한 Llama-2 기반 모델과 Transformer 모델의 비교 분석", 한국인공지능융합기술학회 추계학술대회, 2025. (*: Equally contributed)

[D21] 정민규*, 유지원*, 정원식, "사전학습 EEG 인코더의 효율적 미세조정을 통한 제로샷 EEG 반응시간 예측 모델", 한국인공지능융합기술학회 추계학술대회, 2025. (*: Equally contributed) (최우수논문상)

[D20] 박찬양*, 안치현*, 정원식, "곡선 인식 성능 향상을 위한 선택적 헤더 미세학습 기반 경량 하이브리드 차선 인식 모델", 한국인공지능융합기술학회 추계학술대회, 2025. (*: Equally contributed)

[D19] 조수연, 정원식, "STL 분석 기반 3D 프린팅 자동 견적 및 위험 탐지 AI 챗봇 시스템 개발", 한국인공지능융합기술학회 추계학술대회, 2025.

[D18] 박유빈, 정원식, "설명 가능한 위조 이미지 탐지를 위한 Grad-CAM과 BLIP-2 기반 얼굴 합성 이미지 분석", 한국인공지능융합기술학회 추계학술대회, 2025.

[D17] 사진혁, 정원식, "전이 학습을 적용한 YOLOv11 경량 모델의 꿀벌응애 자동 탐지 성능 분석", 한국인공지능융합기술학회 추계학술대회, 2025.

[D16] 강준혁, 정원식, "StyleGAN2 기반 피부암 병변의 시계열적 진행 시뮬레이션에 관한 연구", 한국인공지능융합기술학회 추계학술대회, 2025. (우수논문상)

[D15] 박찬양*, 정민규*, 정원식, "한국어 음성 인식 성능 향상을 위한 LoRA 기반 Whisper-Tiny 모델 연구", 한국지식정보기술학회, 2025. (*: Equally contributed)

[D14] 김상훈*, 김정훈*, 정원식, "Swin Transformer 기반 주파수 정보 통합을 활용한 생성형 AI 이미지 판별", 한국지식정보기술학회, 2025. (*: Equally contributed)

[D13] 정아영, 정원식, "YOLOv11 기반 객체 탐지 성능 향상을 위한 저조도 영상 보정 기법의 비교 분석에 관한 연구", 한국지식정보기술학회, 2025.

[D12] 서재석*, 장근혁*, 정원식, "흉부 X-ray 영상 기반 자동 진단 보고서 생성을 위한 R2Gen과 A3Net 모델의 성능 비교 분석에 관한 연구", 한국지식정보기술학회, 2025. (*: Equally contributed)

[D11] 정원식*, 김시은*, 김준표, 장혜민, 박채정, 김희진, 나덕렬, 서상원†, 석흥일†, “Predictive Modeling of Personalized Clinical Outcome Trajectories in Mild Cognitive Impairment,” 대한치매학회, 2023. (*: Equally contributed, †: co-corresponding)

[D10] 오관석, 허다운, A.W. Mulyadi, 정원식, 강은송, 석흥일, “Quantitatively Explainable Counterfactuals for Alzheimer’s Disease Prediction,” 한국인공지능학회 추계 학술대회, 2023.

[D9] W. Jung, E. Jeon, E. Kang, and H.-I. Suk, “Explanation-Guided Inter-Regional Relation Learning and Personalized Regions Selection for ASD Diagnosis,” Korean AI Association (KAIA), 2023 (Poster, Best Paper Award)

[D8] 정승우, 정원식, 손정효, 석흥일, “Monotonicity-Enhanced Deep Geometric Learning for Alzheimer’s Disease Progression Analysis,” 2023 KHBM 추계학술대회, 2023.

[D7] 정원식, 전은진, 강은송, 석흥일, “Improving ASD Diagnosis via Individualized ROI Selection and Explainable Inter-Regional Relation Learning,” 2023 KHBM 추계학술대회, 2023.

[D6] A.W. Mulyadi, W. Jung, K. Oh, J.S. Yoon, and H.-I. Suk, “Topological-aware Prototype Learning for Estimating Explainable Alzheimer’s Disease Likelihood Map," Korean AI Association (KAIA), 2022. (Oral)

[D5] S. Jeong, W. Jung, J. Sohn, and H.-I.l Suk, “Predictive Modeling of Alzheimer’s Disease Progression via Geometric Learning,” Korean AI Association (KAIA), 2022.

[D4] J. Sohn, E. Jeon, W. Jung, E. Kang, and H.-I Suk, “Residual Fine-Grained Attention: Tensor Calibrating to Elaborately Localize An Overall Object,” Korean AI Association (KAIA), 2021.

[D3] W. Jung, E. Kang, and H.-I. Suk, “Federated Learning for Autism Spectrum Disorder Classification,” Korean Society for Human Brain Mapping (KHBM), 2020. (Oral, Best Paper Award)

[D2] W. Jung, E. Jun, and H.-I. Suk, “Modeling AD Progression on Longitudinal AD Biomarkers via Deep Recurrent Model,” Korean AI Association (KAIA), 2020.

[D1] W. Jung and H.-I. Suk, “Alzheimer’s Disease Progression Modeling using Missing Value Imputation through Data-Driven Temporal and Multivariate Correlation,” Proc. of 2019 KIISE Korea Computer Congress (KCC), 2019. (Oral, Best Paper Award)