Ph.D. in Electrical Engineering, Stanford University (2014)
M.S. in Statistics, Stanford University (2012)
M.S. in Electrical Engineering, Stanford University (2008)
M.S. in Electrical Engineering and Computer Science, Seoul National University (2006)
B.S. in Electrical Engineering, Seoul National University (2004)

Assistnat Professor, Seoul National University of Science and Technology (2023.9- )
Senior Research Scientist, Korea Institute of Science and Technology (2017.3-2023.8)
Research Staff Member, Samsung Advanced Institute of Technology (2014.4-2017.2)

◾ H Jang, NY Han, J Kwon, H Seo, BJ Park, K Choi, “Cyclic Conditional Diffusion Models for CT-to-MR Synthetic Image Segmentation with Misaligned Image Pairs”, Expert Systems With Applications, 2025.

◾ K Choi, SH Kim, S Kim, “Self-Supervised Learning in Projection Domain for Low-Dose Cone-Beam CT”, Medical Physics, 2023.

◾ K Choi, S Kim, J Lim, “Self-Supervised Inter- and Intra-Slice Correlation Learning for Low-Dose CT Image Restoration without Ground Truth”, Expert Systems with Applications, 2022.

◾ K Choi, S Kim, J Lim, “StatNet: Statistical Image Restoration for Low-Dose CT using Deep Learning”, IEEE Journal of Selected Topics in Signal Processing, 2020.

◾ K Choi, R Li, H Nam, L Xing, “A Fourier-based Compressed Sensing Technique for Accelerated CT Image Reconstruction using First-Order Methods”, Physics in Medicine and Biology, 2014.

◾ K Choi, J Wang, L Zhu, T-S Suh, S Boyd, L Xing, “Compressed Sensing based Cone-Beam Computed Tomography Reconstruction with a First-Order Method”, Medical Physics, 2010.

◾ Cyclic Conditional Diffusion Models for CT-to-MR Synthetic Image Segmentation with Misaligned Image Pairs, Expert Systems With Applications, 2025.

◾ Artificial Intelligence Model for Detection of Colorectal Cancer on Routine Abdominopelvic CT Examinations: A Training and External-Testing Study, American Journal of Roentgenology, 2025.

◾ Self-Supervised Learning-based CT Image Denoising and Reconstruction: A Review, Biomedical Engineering Letters, 2024. (invited paper)

◾ Self-Supervised Learning in Projection Domain for Low-Dose Cone-Beam CT, Medical Physics, 2023.

◾ Self-Supervised Inter- and Intra-Slice Correlation Learning for Low-Dose CT Image Restoration without Ground Truth, Expert Systems with Applications, 2022.

◾ Prediction of the histology of colorectal neoplasm in white light colonoscopic images using deep learning algorithms, Scientic Reports, 2021.

◾ StatNet: Statistical Image Restoration for Low-Dose CT using Deep Learning”, IEEE Journal of Selected Topics in Signal Processing, 2020.

◾ A Subband-Specic Deconvolution Model for MTF Improvement in CT, Journal of Healthcare Engineering, 2017.

◾ A Distance-Driven Deconvolution Method for CT Image-Resolution Improvement, Journal of the Korean Physical Society, 2016.

◾ A Preliminary Study of an Image Synthesis Method to Simulate the Change in Incident X-ray Spectrum using Thickness Information, Journal of the Korean Physical Society, 2016.

◾ A Fourier-based Compressed Sensing Technique for Accelerated CT Image Reconstruction using First-Order Methods, Physics in Medicine and Biology, 2014.

◾ First Study of On-Treatment Volumetric Imaging During Respiratory Gated VMAT, Medical Physics, 2013. (Highlighted as Editor's Pick)

◾ Enhancement of Four-Dimensional Cone-Beam Computed Tomography by Compressed Sensing with Bregman Iteration, Journal of X-Ray Science and Technology, 2013.

◾ Total-Variation Regularization based Inverse Planning for Intensity Modulated Arc Therapy, Technology of Cancer Research & Treatment, 2012.

◾ Compressed Sensing based Cone-Beam Computed Tomography Reconstruction with a First-Order Method, Medical Physics, 2010.

◾ Y Kim, S Park, H Kim, SS Kim, JS Lim, S Kim, K Choi, H Seo, “A Bounding-Box Regression Model for Colorectal Tumor Detection in CT Images Via Two Contrary Networks”, Annual International Conference
of the IEEE Engineering in Medicine and Biology Society (EMBC), 2022.

◾ K Choi, “Self-supervised Projection Denoising for Low-Dose Cone-Beam CT”, Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021.

◾ J Kwon, K Choi, “Weakly Supervised Attention Map Training for Histological Localization of Colonoscopy Images”, Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021.

◾ K Choi, SJ Choi, ES Kim, “Computer-Aided diagonosis for colorectal cancer using deep learning with visual explanations”, Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020.

◾ K Choi, S Kim, “Statistical Image Restoration for Low-Dose CT using Convolutional Neural Networks”, Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020.

◾ J Kwon, K Choi, “Trainable multi-contrast windowing for liver CT segmentation”, IEEE International Conference on Big Data and Smart Computing (BigComp), 2020. (Best Paper Award)

◾ K Choi, M Vania, S Kim, “Semi-Supervised Learning for Low-Dose CT Image Restoration with Hierarchical Deep Generative Adversarial Network (HD-GAN)”, Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019.

◾ K Choi, S Kim, J Lim “Real-time image reconstruction for low-dose CT using deep convolutional generative adversarial networks (GANs)”, SPIE Medical Imaging, 2018.

◾ K Choi, J Wang, L Zhu, T Suh, S Boyd, L Xing, “Compressed Sensing with a First-Order Method for Low-Dose Cone-Beam CT Reconstruction”, International Conference on the Use of Computers in Radiation Therapy (ICCR), 2010. (oral presentation)

◾ K Choi and S Choi, “CLPC: Cross-Layer Product Code for Video Multicast over WLAN”, MediaWiN Workshop of European Wireless, 2006. (invited paper)

◾ L Xing, J Qian, K Choi, T-S Suh, “Three- and Four-dimensional Morphological Imaging for Adaptive Radiation Therapy Planning”, chapter 2 in Adaptive Radiation Therapy, CRC Press, 2011.

◾ S Choi and K Choi, “Reliable Multicast for Wireless Local Area Networks,” chapter 4 in Resource, Mobility and Security Management in Wireless Networks and Mobile Communications, CRC Press, 2006.

◾ Image processing apparatus and method based on deep learning and neural network learning, 11,341,375(US), 2022

◾ Apparatus and method to train autonomous driving model, and autonomous driving apparatus, 10,791,979(US), 2020

◾ Tomography apparatus and method for reconstructing tomography image thereof, 10,339,675(US), 2019

◾ Apparatus and method for object recognition and for training object recognition model, 10,133,938(US), 2018

◾ AI 클러스터 혁신생태계 확산 (연구책임자-참여기관), 2025-2030, 서울RISE사업, 서울연구원

◾ 순환적 생성형 인공지능과 자기지도 학습 모델 개발을 통한 저선량 복부CT영상으로부터의 연부조직 예측 (연구책임자), 2025-2028, 개인기초연구사업(우수신진), 한국연구재단

◾ 인간지향 체어사이드 K덴탈 솔루션개발 (연구책임자-참여기관), 2020-2024, 시장친화형 글로벌 경쟁력확보 제품개발사업, 범부처의료기기연구개발사업단

◾ AI기반 생체정보 분석기술 개발 (연구책임자), 2020-2022, 미래원천기술개발사업, 한국과학기술연구원