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ksoh97/README.md

Kwanseok Oh (Kwan Seok Oh)

I am a Ph.D candidate in Artificial Intelligence at Korea University.

🧐 Research Interests

  • Medical/Computer Vision
  • Generalizable/Foundational Modeling, Explainable AI (XAI), Representation Learning, and Biomedical image analysis

🛠️ Skills

  • Programming Language: Python, Visual C/C++, Javascript, R, and MATLAB
  • Deep Learning Framework: TensorFlow/Keras, PyTorch

📰 Publications

  • Multi-Scale Minimal Sufficient Representation Learning for Domain Generalization in Sleep Staging, Under Review

  • Integrating Multimodal Large Language Models with Adaptive Context-Aware Decoding for Robust Medical Image Segmentation, Under Review

  • DyMix: Dynamic Frequency Mixup Scheduler- based Unsupervised Domain Adaptation for Enhancing Alzheimer’s Disease Identification, Under Review

  • IdenBAT: Disentangled Representation Learning for Identity-Preserved Brain Age Transformation, Under Review

  • FIESTA: Fourier-based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation, Under Review

  • Transferring Ultra-high Field Feature Representations for Intensity-Guided Brain Segmentation of Low Field Magnetic Resonance Imaging, IEEE TNNLS

  • [2025] A Quantitatively Interpretable Model for Alzheimer’s Disease Prediction using Deep Counterfactuals, NeuroImage, JCR-IF: 4.7, Neuroimaging: 1/15

  • [2025] Linear Fusion-Based Dual-MR Contrast Enhancement for Improved Choroid Plexus Segmentation, ISMRM'25, Oral Presentation

  • [2024] Domain Generalization for Medical Image Analysis: A Review, Proceedings of the IEEE, JCR-IF: 23.2, Engineering, Electrical & Electronic 2/353

  • [2024] Frequency Mixup Manipulation based Unsupervised Domain Adaptation for Brain Disease Identification, ACPR'24, Oral Presentation

  • [2023] Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer’s Disease Diagnosis Model, IEEE TPAMI, JCR-IF: 24.314, Computer Science & Artificial Intelligence: 2/144

  • [2023] Age-Aware Guidance via Masking-Based Attention in Face Aging, CIKM'23, 27.4% acceptance rate

  • [2023] Estimating Explainable Alzheimer’s Disease Likelihood Map via Clinically-Guided Prototype Learning, NeuroImage, JCR-IF: 7.4, Neuroimaging: 2/14

  • [2022] Quantifying Explainability of Counterfactual-Guided MRI Feature for Alzheimer’s Disease Prediction, MedNeurIPS'22

  • [2022] Clinically-guided Prototype Learning and Its Use for Explanation in Alzheimer’s Disease Identification, MedNeurIPS'22

  • [2022] A Novel Knowledge Keeper Network for 7T-Free But 7T-Guided Brain Tissue Segmentation, MICCAI'22

  • [2020] VIGNet: A Deep Convolutional Neural Network for EEG-based Driver Vigilance Estimation, IEEE IWCBCI'20

  • Domestic Conferences (1 KHBM, 1 IEIE, 1 IPIU, 1 KSEE, 3 KAIA, 1 CKMS, 2 KCR)

📝 Patents & Software Registration

  • [US Patent Registration] METHOD AND APPARATUS FOR REASONING AND REINFORCING DECISION IN BRAIN DISEASE DIAGNOSIS MODEL (No. 17714390)
  • [US Patent Application] BRAIN IMAGE-BASED QUANTITATIVE BRAIN DISEASE PREDICTION METHOD AND APPARATUS (No. 18212261)
  • [KR Patent Registration] 알츠하이머병 진단 모델의 결정을 해석하고 강화하는 방법 및 장치 (No. 10-2593036)
  • [KR Patent Registration] 분류 결과 설명이 가능한 반 사실적 맵 생성 방법 및 그 장치 (No. 10-2496769)
  • [KR Patent Registration] 자기공명영상 기반 뇌질환 예측 방법 및 장치 (No. 10-2760232)
  • [KR Patent Application] 뇌혈관계 다중 분할 기반의 뇌동맥류 검출 장치 (No. 10-2024-0169281)
  • [KR Patent Application] 불확실성 지침을 활용한 푸리에 기반 의미론적 증강 장치 (No. 10-2024-0112696)
  • [Technology Transfer/KR Patent Registration] 반려동물 관리시스템 및 방법 (No. 10-1983101)
  • [Software Registration] 뇌 영상 기반 설명 가능한 알츠하이머병 조기 예측 프로그램 (No. No. 114471-0002565)

🏅 Awards & Honors

  • [2024] TopCow2024 Grand Challenge, MICCAI - Detection (CTA&MRA): 2nd; Segmentation (CTA&MRA): 3rd; Classification (CTA&MRA): 4th
  • [2023] KU Achievement Award 2022, Korea University - Awarded to the Best Student in Each Department
  • [2023] 4th JW Foundation Fundamental Scientist Scholarship, JW Foundation
  • [2023] NAVER Ph.D. Fellowship Award 2022, Naver Corp.
  • [2022] Qualcomm Innovation Fellowship Korea 2022, Qualcomm AI Research
  • [2022] KT Business Startup Idea Challenge, KT Enterprise - Excellence Award (2nd)
  • [2022] Announcement of Outstanding Research Results at the Graduate School of Artificial Intelligence Symposium (Coex Grand Room), Artificial Intelligence Graduate School Council (AIGSC)
  • [2022] DataHub Team BK Fellowship Scholarship, Korea University
  • [2022] KU Graduate Student Achievement Scholarship, Korea University

🧑🏻‍💻 Peer Review

  • Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Medical Imaging (TMI), Neural Networks and Learning Systems (TNNLS), Artificial Intelligence Review, Neural Networks
  • Conference: International Conference on Computer Vision and Pattern Recognition (CVPR), The AAAI Conference on Artificial Intelligence (AAAI), International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), International Conference on Medical Imaging with Deep Learning (MIDL)

✉️ Contact

Pinned Loading

  1. LEAR LEAR Public

    Tensorflow implementation of "Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Model" [IEEE TPAMI 2023]

    Python 6 1

  2. LiCoL LiCoL Public

    Forked from ku-milab/LiCoL

    Tensorflow implementation of "A Quantitatively Interpretable Model for Alzheimer’s Disease Prediction Using Deep Counterfactuals" [MedNeurIPS 2022], [NeuroImage 2025]

    Python 1

  3. GMBA GMBA Public

    Forked from ku-milab/GMBA

    Pytorch implementation of "Age-Aware Guidance via Masking-Based Attention in Face Aging" [CIKM 2023]

    Python

  4. FIESTA FIESTA Public

    Pytorch implementation of "FIESTA: Fourier-based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation" [Submitted to MedIA]

    Python 2 1

  5. UHF-guided_segmentation UHF-guided_segmentation Public

    Forked from ku-milab/UHF-guided_segmentation

    PyTorch implementation of "Transferring Ultra-high Field Representations for Intensity-Guided Brain Segmentation of Low Field MRI" [MICCAI 2022], [Submitted to IEEE TNNLS]

    Python 1

  6. BIN BIN Public

    Tensorflow implementation of "Born Identity Network: Multi-way Counterfactual Map Generation to Explain a Classifier's Decision"

    Python 6