Please refer to my google scholar profile for the updated list: https://scholar.google.com/citations?user=-M9AJGkAAAAJ&hl=en
Refereed Papers
Youngjin Yoo, Gengyan Zhao, Andreea E. Sandu, Thomas J. Re, Jyotipriya Das, Wang Hesheng, Michelle Kim, Colette Shen, Yueh Lee, Douglas Kondziolka, Mohannad Ibrahim, Jun Lian, Rajan Jain, Tong Zhu, Hemant Parmar, James M. Balter, Yue Cao, Eli Gibson, and Dorin Comaniciu, "The importance of data domain on self-supervised learning for brain metastasis detection and segmentation, " SPIE Medical Imaging 2023: Computer-Aided Diagnosis (Vol. 12465).
Gengyan Zhao, Youngjin Yoo, Thomas J. Re, Jyotipriya Das, Wang Hesheng, Michelle Kim, Colette Shen, Yueh Lee, Douglas Kondziolka, Mohannad Ibrahim, Jun Lian, Rajan Jain, Tong Zhu, Hemant Parmar, James M. Balter, Yue Cao, Eli Gibson, and Dorin Comaniciu, "3D-2D GAN based brain metastasis synthesis with configurable parameters for fully 3D data augmentation, " SPIE Medical Imaging 2023: Image Processing (Vol. 12464, pp. 115-120). "Oral presentation"
Ghesu, Florin C., Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Dominik Neumann, Pragneshkumar Patel, R. S. Vishwanath et al. "Contrastive self-supervised learning from 100 million medical images with optional supervision." Journal of Medical Imaging 2022 Nov 1;9(6):064503.
Gibson, Eli, Bogdan Georgescu, Pascal Ceccaldi, Pierre-Hugo Trigan, Youngjin Yoo, Jyotipriya Das, Thomas J. Re et al. "Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans." Radiology: Artificial Intelligence 4, no. 3 (2022): e210115.
Youngjin Yoo, Pascal Ceccaldi, Siqi Liu, Thomas J. Re, Yue Cao, James M. Balter, and Eli Gibson. "Evaluating deep learning methods in detecting and segmenting different sizes of brain metastases on 3D post-contrast T1-weighted images." Journal of Medical Imaging 8, no. 3 (2021): 037001.
Nael, Kambiz, Eli Gibson, Chen Yang, Pascal Ceccaldi, Youngjin Yoo, Jyotipriya Das, Amish Doshi et al. "Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks." Scientific reports 11, no. 1 (2021): 1-10.
Nguyen P. Nguyen, Youngjin Yoo, Andrei Chekkoury, Eva Eibenberger, Thomas J. Re, Jyotipriya Das, Abishek Balachandran, Yvonne W. Lui, Pina C. Sanelli, Thomas J. Schroeppel, Uttam Bodanapally, Savvas Nicolaou, Tommi A. White, Filiz Bunyak, Dorin Comaniciu, and Eli Gibson. "Brain midline shift detection and quantification by a cascaded deep network pipeline on non-contrast computed tomography scans," International Conference on Computer Vision (ICCV) AI-enabled Medical Image Analysis Workshop 2021. Accepted.
Youngjin Yoo, Pascal Ceccaldi, Siqi Liu, Thomas J. Re, Yue Cao, James M. Balter and Eli Gibson, "Evaluating deep learning methods in detecting and segmenting different sizes of brain metastases on 3D post-contrast T1-weighted images", SPIE Journal of Medical Imaging 8 (3), 037001, 2021.
Kambiz Nael, Eli Gibson, Chen Yang, Pascal Ceccaldi, Youngjin Yoo, Jyotipriya Das, Amish Doshi, Bogdan Georgescu, Nirmal Janardhanan, Benjamin Odry, Mariappan Nadar, Michael Bush, Thomas J. Re, Stefan Huwer, Sonal Josan, Heinrich von Busch, Heiko Meyer, David Mendelson, Burton P. Drayer, Dorin Comaniciu & Zahi A. Fayad. Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks. Scientific Reports ;11(1):1-0. 2021.
Eduardo J Mortani Barbosa, Bogdan Georgescu, Shikha Chaganti, Gorka Bastarrika Aleman, Jordi Broncano Cabrero, Guillaume Chabin, Thomas Flohr, Philippe Grenier, Sasa Grbic, Nakul Gupta, François Mellot, Savvas Nicolaou, Thomas Re, Pina Sanelli, Alexander W Sauter, Youngjin Yoo, Valentin Ziebandt, Dorin Comaniciu. "Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort," European Radiology. 2021 May 1:1-1.
Po-Jui Lu, Youngjin Yoo, Reza Rahmanzadeh, Riccardo Galbusera, Matthias Weigel, Pascal Ceccaldi, Thanh D Nguyen, Pascal Spincemaille, Yi Wang, Alessandro Daducci, Francesco La Rosa, Meritxell Bach Cuadra, Robin Sandkühler, Kambiz Nael, Amish Doshi, Zahi A Fayad, Jens Kuhle, Ludwig Kappos, Benjamin Odry, Philippe Cattin, Eli Gibson, Cristina Granziera. "GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology." NeuroImage: Clinical 29 (2020): 102522.
Dao, Elizabeth, Tam, Roger, Hsiung, Ging-Yuek, ten Brinke, Lisanne, Crockett, Rachel, Barha, Cindy K., Yoo, Youngjin, Al Keridy, Walid, Doherty, Stephanie H., Laule, Cornelia, MacKay, Alex L., Liu-Ambrose, Teresa, ‘Exploring the Contribution of Myelin Content in Normal Appearing White Matter to Cognitive Outcomes in Cerebral Small Vessel Disease’. Journal of Alzheimer's Disease, 1 Jan. 2021 : 1 – 11.
Florin C Ghesu, Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Eli Gibson, RS Vishwanath, Abishek Balachandran, James M Balter, Yue Cao, Ramandeep Singh, Subba R Digumarthy, Mannudeep K Kalra, Sasa Grbic, Dorin Comaniciu, "Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment," Medical Image Analysis. 2020 Oct 14:101855.
Shikha Chaganti, Philippe Grenier, Abishek Balachandran, Guillaume Chabin, Stuart Cohen, Thomas Flohr, Bogdan Georgescu, Sasa Grbic, Siqi Liu, François Mellot, Nicolas Murray, Savvas Nicolaou, William Parker, Thomas Re, Pina Sanelli, Alexander W Sauter, Zhoubing Xu, Youngjin Yoo, Valentin Ziebandt, Dorin Comaniciu, "Automated quantification of CT patterns associated with COVID-19 from chest CT." Radiology: Artificial Intelligence 2, no. 4 (2020): e200048.
Mahmoud Mostapha, Boris Mailhe, Xiao Chen, Pascal Ceccaldi, Youngjin Yoo, Mariappan Nadar, "Braided Networks for Scan-Aware MRI Brain Tissue Segmentation", IEEE International Symposium on Biomedical Imaging (ISBI), pp. 136-139, 2020.
Morris, Sarah R., Richard Davis Holmes, Adam V. Dvorak, Hanwen Liu, Youngjin Yoo, Irene M. Vavasour, Silvia Mazabel et al. "Brain myelin water fraction and diffusion tensor imaging atlases for 9‐10 year‐old children." Journal of Neuroimaging 30, no. 2 (2020): 150-160.
Oh, J., Ontaneda, D., Azevedo, C., Klawiter, E.C., Arnold, D., Bakshi, R., Calabresi, P.A., Crainiceanu, C.M., Dewey, B., Freeman, L., Gauthier, A., Henry, R., Inglese, M., Kolind, S., Li, D., Mainero, C., Menon, R., Nair, G., Narayanan, S., Nelson, F., Pelletier, D., Rauscher, A., Rooney, W., Sati, P., Schwartz, P., Shinohara, R.T., Tagge, I., Traboulsee, A., Wang, Y. Yoo, Y., Yousry, T., Zhang, Y., Sicotte, N., and Reich, D.S., on behalf of the North American Imaging in Multiple Sclerosis Cooperative. Imaging Outcome Measures for Proof-of-Concept Clinical Trials Assessing Neuroprotection and/or Repair in MS: A Consensus Statement from the NAIMS Cooperative. Neurology, 92(11):519-533, 2019.
Y. Yoo, L.Y.W. Tang, T. Brosch, D.K.B. Li, S. Kolind, I. Vavasour, A. Rauscher, A. MacKay, A. Traboulsee and R. Tam. Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls. NeuroImage: Clinical, 17:169-178, 2018.
Y. Yoo, L.Y.W. Tang, S. Kim, H. Kim, L.E. Lee, D.K.B. Li, S. Kolind, A. Traboulsee, R. Tam. Hierarchical multimodal fusion of deep-learned lesion and tissue integrity features in brain MRIs for distinguishing neuromyelitis optica from multiple sclerosis. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Part III, pages 480-488, 2017. (travel award)
A. Porisky, T. Brosch, E. Ljungberg, L.Y.W. Tang, Y. Yoo, B. De Leener, A. Traboulsee, J. Cohen-Adad, and R. Tam. Grey matter segmentation in spinal cord MRIs via 3D convolutional encoder networks with shortcut connections. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Deep Learning in Medical Image Analysis (DLMIA), pages 330-337, 2017.
Y. Yoo, L.Y.W. Tang, D.K.B. Li, L. Metz, S. Kolind, A. Traboulsee and R. Tam. Deep learning of brain lesion patterns and user-defined clinical and MRI features for predicting conversion to multiple sclerosis from clinically isolated syndrome. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 7(3): 250-259, 2017.
E. Ljungberg, I. Vavasour, R. Tam, Y. Yoo, A. Rauscher, D.K.B. Li, A. Traboulsee, A. MacKay, S. Kolind. Rapid myelin water imaging in human cervical spinal cord. Magnetic Resonance in Medicine, 78(4):1482-1487, 2016.
Y. Yoo, L.Y.W. Tang, T. Brosch, D.K.B. Li, L. Metz, A. Traboulsee, and R. Tam. Deep learning of brain lesion patterns for predicting future disease activity in patients with early symptoms of multiple sclerosis. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Deep Learning in Medical Image Analysis (DLMIA), pages 86-94, 2016. (oral presentation)
L.Y.W. Tang, T. Brosch, X. Liu, Y. Yoo, A. Traboulsee, D.K.B. Li and R. Tam. Corpus callous segmentation in brain MRIs via robust target-localization and joint supervised feature extraction and prediction. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Part II, pages 406-414, 2016.
T. Brosch, L.Y.W. Tang, Y. Yoo, D.K.B. Li, A. Traboulsee, and R. Tam. Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Transactions on Medical Imaging (Special Issue on Deep Learning in Medical Imaging), 35(5):1229-1239, 2016.
T. Brosch, Y. Yoo, L.Y.W. Tang, and R. Tam. Deep Learning of Brain Images and its Application to Multiple Sclerosis. In G. Wu, D. Shen, and M. Sabuncu, editors, Machine Learning and Medical Imaging, Chapter 3. Elsevier, 2016
T. Brosch, Y. Yoo, L.Y.W. Tang, D.K.B. Li, A. Traboulsee, and R. Tam. Deep convolutional encoder networks for multiple sclerosis lesion segmentation. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Part III, pages 3-11, 2015.
Y. Yoo, T. Prasloski, I. Vavasour, A. MacKay, A. Traboulsee, D.K.B. Li, and R. Tam. Fast computation of myelin maps from MRI T2 relaxation data using multicore CPU and graphics card parallelization. Journal of Magnetic Resonance Imaging, 41(3):700-707, 2015.
Y. Yoo, T. Brosch, A. Traboulsee, D.K.B. Li, and R. Tam. Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Machine Learning in Medical Imaging (MLMI), pages 113-120, 2014. (oral presentation)
T. Brosch, Y. Yoo, D.K.B. Li, A. Traboulsee, and R. Tam. Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Part II, pages 463-470, 2014.
Y. Yoo and R. Tam. Non-local spatial regularization of MRI T2 relaxation images for myelin water quantification. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Part I, pages 614-621, 2013.
B. Park, S. Han, W. Choe, J. Lim, Y. Yoo, and S. Lee. Low light imaging system with expanding spectrum band for digital camera. In Proceedings of IEEE International Conference on Consumer Electronics (ICCE), pages 35-36, 2012.
Y. Yoo, W. Choe, and S. Lee. Wide-band image guided visible-band image enhancement. In Proceedings of IEEE International Conference on Image Processing (ICIP), pages 3405-3408, 2011.
Y. Yoo, K. Lee, W. Choe, S. Park, and S. Lee, Chang-Yong Kim. A digital ISO expansion technique for digital cameras. In Proceedings of IS&T/SPIE Electronic Imaging, pages 75370U-75370U-12, 2010.
Y. Yoo, W. Choe, J. Kwon, S. Park, S. Lee, and C. Kim. Low-light imaging method with visible-band and wide-band image pair. In Proceedings of IEEE International Conference on Image Processing (ICIP), pages 2773-2776, 2009.
Y. Yoo, H. Wey, S. Lee, and C. Kim. Profile based fast noise estimation and high ISO noise reduction for digital cameras. In Proceedings of IS&T/SPIE Electronic Imaging, pages 68170B-68170B-12, 2008. (oral presentation)
Y. Yoo, S. Lee, W. Choe, and C. Kim. CMOS image sensor noise reduction method for image signal processor in digital cameras and camera phones. In Proceedings of IS&T/SPIE Electronic Imaging, pages 65020S-65020S-10, 2007.
Abstracts and Short Papers
Youngjin Yoo, Eli Gibson, Gengyan Zhao, Andreea E. Sandu, Thomas J. Re, Jyotipriya Das, Wang Hesheng, Michelle Kim, Colette Shen, Yueh Lee, Douglas Kondziolka, Mohannad Ibrahim, Jun Lian, Rajan Jain, Tong Zhu, Hemant Parmar, Dorin Comaniciu, James M. Balter and Yue Cao, "An automated brain metastasis detection and segmentation system from MRI with a large multi-institutional dataset, " ASTRO Annual Meeting 2023, Accepted. "Oral presentation"
P.-J. Lu, R. Rahmanzadeh, R. Galbusera, M. Weigel, Y. Yoo, P. Ceccaldi, Y. Wang, J. Kuhle, L. Kappos, P. Cattin, B. Odry, E. Gibson, C. Granziera, "Attention-based convolutional network quantifying the importance of quantitative MR metrics in the multiple sclerosis lesion classification", In Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM) Scientific Meeting, 2020.
Maxime Bertrait, Pascal Ceccaldi, Boris Mailhé, Youngjin Yoo, Mariappan S. Nadar, "3D Dual Recursive Refiner Network for Robust Segmentation: Application to Brain Extraction", In Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM) Scientific Meeting, 2020.
L.Y.W. Tang, E. Lapointe, J. Chan, Y. Yoo, L.E. Lee, A. Riddehough, S. Kolind, S. Kim, H. Kim, D.K.B. Li, A. Traboulsee, R. Tam. Machine learning for automated analysis of brain lesional patterns for NMOSD-MS differential diagnosis. In Proceedings of Canadian Association of Radiologists Annual Meetings, Artificial Intelligence in Radiology, 2018. (first place in the scientific research project abstract category)
L.Y.W. Tang, S. Garg, Y. Yoo, A. Riddehough, S. Kolind, A. Traboulsee, L. Metz, D.K.B. Li, R. Tam. Corpus callosum atrophy develops within three months since CIS trial enrolment. In Proceedings of European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), 2017.
Y. Yoo, L.Y.W. Tang, S. Kim, H. Kim, L.E. Lee, D.K.B. Li, S. Kolind, A. Traboulsee, R. Tam. Deep learning of brain MRI lesion and diffusion tensor imaging features for distinguishing neuromyelitis optica spectrum disorder from multiple sclerosis. In Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM) White Matter Disease Workshop, 2017.
L.Y.W. Tang, Y. Yoo, T. Brosch, L.E. Lee, S. Kim, H. Kim, D.K.B. Li, S. Kolind, A. Traboulsee, R. Tam. DTI measures in the corpus callosum demonstrate group differences between multiple sclerosis and neuromyelitis optical spectrum disorder. In Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM) White Matter Disease Workshop, 2017.
Y. Yoo, L.Y.W. Tang, T. Brosch, D.K.B. Li, S. Kolind, I. Vavasour, A. Rauscher, A. MacKay, A. Traboulsee, and R. Tam. Deep learning of joint myelin-T1w MRI features on normal-appearing brain tissues distinguishes multiple sclerosis from healthy controls. In Proceedings of Pan-Asian Committee for Treatment and Research in Multiple Sclerosis (PACTRIMS), 2016. (oral and poster presentation)
M. Le, A. Rauscher, T. Brosch, Y. Yoo, L. Tang, M. Jarrett, A. Traboulsee, D.K.B. Li, and R. Tam. FLAIR^2 improves automatic lesion segmentation over FLAIR in MS patients. In Proceedings of European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), 2016.
J. Schubert, E. MacMillan, I. Vavasour, D. Leppert, N. Seneca, E. Vianna, A. Dayakanchuk, Y. Yoo, R. Tam, S. Kolind, and A. Traboulsee. Myelin damage in relapsing multiple sclerosis is associated with decreased N-acetylaspartate and creatine concentrations. In Proceedings of the American Academy of Neurology (AAN) Annual Meeting, 2016.
E. Ljungberg, I. Vavasour, R. Tam, Y. Yoo, A. Rauscher, D.K.B. Li, A. Traboulsee, A. MacKay, and S. Kolind. Myelin water imaging in human cervical spinal cord using a 3D-GRASE sequence. In Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM) Scientific Meeting, 2016.
E. MacMillan, J. Schubert, I. Vavasour, E. Vianna, A. Dzyakanchuk, Y. Yoo, R. Tam, S. Kolind, and A. Traboulsee. N-acetylaspartate and creatine decrease with myelin damage in relapsing multiple sclerosis. In Proceedings of European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), 2015.
Invited Talks and Meeting Presentations
Machine learning of brain MR images and its applications to multiple sclerosis. Citywide Round Seminar Series, Faculty of Medicine, University of British Columbia, Vancouver, Canada, Dec. 2016.
Deep learning of joint myelin-T1w MRI features on normal-appearing brain tissues distinguishes multiple sclerosis from healthy controls. North American Imaging in Multiple Sclerosis (NAIMS) Meeting, Toronto, Canada, Nov. 2016.
Machine learning for predicting the risk of conversion to the 2005 McDonald criteria in patients with early symptoms of multiple sclerosis. North American Imaging in Multiple Sclerosis (NAIMS) Meeting, Toronto, Canada, Nov. 2016.
Patents
Youngjin Yoo, Eli Gibson, Gengyan Zhao, Long Xie, Boris Mailhe, Dorin Comaniciu and Thomas Re. "A high-resolution 3D brain MRI generative system for pathological aging monitoring and neuro-degenerative abnormality detection." U.S. Patent Application 2023E17533 US 2023.
Youngjin Yoo, Eli Gibson, Pragneshkumar Patel, Gianluca Paladini, Poikavila Ullaskrishnan and Dorin Comaniciu. "Adaptive aggregation for federated learning.'' U.S. Patent Application 17449165, filed March 30, 2023.
Youngjin Yoo, Gianluca Paladini, Eli Gibson, Pragneshkumar Patel, and Poikavila Ullaskrishnan. "Privacy-preserving data curation for federated learning.'' U.S. Patent Application 17/449,190, filed March 30, 2023.
Youngjin Yoo, Thomas Re, Eli Gibson, Andrei Chekkoury. Automatic hemorrhage expansion detection from head CT images. US Patent 17211927
Eli Gibson, Bogdan Georgescu, Pascal Ceccaldi, Youngjin Yoo, Jyotipriya Das, Thomas Re, Eva Eibenberger, Andrei Chekkoury, Barbara Brehm, Thomas Flohr, Dorin Comaniciu, Pierre-Hugo Trigan. Machine learning for automatic detection of intracranial hemorrhages with uncertainty measures from ct images. US Patent 17249783
Y. Yoo, P. Cecadilli and E. Gibson. Automatic detection of lesions in medical images using 2D and 3D deep learning networks. US Patent 17118668
Nguyen Nguyen, Y. Yoo, P. Cecadilli and E. Gibson. Automated estimation of midline shift in brain CT images. US Patent 17303932
Mostapha, Mahmoud, Boris Mailhe, Mariappan S. Nadar, Pascal Ceccaldi, and Youngjin Yoo. "Protocol-Aware Tissue Segmentation in Medical Imaging." U.S. Patent Application 15/929,430, filed April 1, 2021.
Y. Yoo, P. Cecadilli, E. Gibson and M. S. Nadar. Saliency mapping by feature map reduction and perturbation modeling. US patent pending, US Patent App. 16707209, 2019.
Y. Yoo and M. S. Nadar. Automated uncertainty estimation of lesion segmentation. US Patent App. 16/355,881, 2018.
W. Choe, S. Lee, and Y. Yoo. Image sensor and method using near infrared signal. US Patent 9,435,922, 2016.
Y. Yoo, H. Wey, and S. Lee. Noise-reduction method and apparatus. US Patent 9,014,503, 2015.
Y. Yoo, W. Choe, and S. Lee. Method and apparatus for reducing noise. US Patent 8,768,093, 2014.
J. Lim, W. Choe, B. Park, Y. Yoo, and S. Lee. Apparatus and method for processing images. US Patent 8,693,770, 2014.
Y. Yoo, W. Choe, S. Lee, and K. Lee. Apparatus and method for generating high ISO image. US Patent 8,471,928, 2013.
W. Choe, Y. Yoo, and S. Lee. Image processing apparatus and method of noise reduction. US Patent 8,614,746, 2013.
Y. Moon, S. Lee, H. Lee, and Y. Yoo. Method and apparatus for simultaneously reducing various kinds of noises from image. US Patent 8,548,234, 2013.
Y. Yoo, and H. Wey. Apparatus and method for reducing noise from an image. US Patent 8,478,058, 2013.
Y. Yoo, and S. Lee. Noise reduction method, medium, and system. US Patent 8,467,003, 2013.
Y. Yoo, W. Choe, and J. Kwon. Apparatus and method for processing image. US Patent 8,463,069, 2013.
K. Lee, S. Kim, S. Lee, W. Choe, and Y. Yoo. Apparatus and method for generating high sensitivity images in dark environment. US Patent 8,406,560, 2013.
H. Oh, W. Choe, S. Lee, H. Song, S. Park, Y. Yoo, J. Kwon, and K. Lee. Apparatus and method for obtaining motion adaptive high dynamic range image. US Patent 8,379,094, 2013.
Y. Yoo, W. Choe, J. Lim, and S. Lee. Noise reduction apparatus having edge enhancement function and method thereof. US Patent 8,335,395, 2012.
H. Wey, H. Oh, Y. Yoo, and S. Lee. Method and system for processing low-illuminance image. US Patent 8,295,629, 2012.
J. Kwon, S. Lee, D. Chung, W. Choe, and Y. Yoo. Method and apparatus for enhancing image, and image-processing system using the same. US Patent 8,023,763, 2011.
Y. Yoo, H. Lee, D. Park, S. Lee, and S. Lee. Method and apparatus for reducing noise of image sensor. US Patent 8,004,586, 2011.
Y. Yoo, S. Lee, and H. Ok. Apparatus and method for reducing noise of image sensor. US Patent 7,813,583, 2010.
Y. Yoo, and S. Lee. Method and apparatus for estimating noise determination criteria in an image sensor. US Patent 7,734,060, 2010.
Theses
Deep learning for feature discovery in brain MRIs for patient-level classification with applications to multiple sclerosis. Doctoral thesis, The University of British Columbia, 2018.
Robust and fast T2 decay analysis for measuring myelin water in MRI. Master's thesis, The University of British Columbia, 2013. (The developed software is being used for a number of key clinical trials of new multiple sclerosis therapies at UBC's Multiple Sclerosis/MRI Research Group. Most notably, the software was used to show a beneficial treatment effect of Ocrelizumab and Alemtuzumab on myelin repair in separate trials.)
Refereed Papers
Youngjin Yoo, Gengyan Zhao, Andreea E. Sandu, Thomas J. Re, Jyotipriya Das, Wang Hesheng, Michelle Kim, Colette Shen, Yueh Lee, Douglas Kondziolka, Mohannad Ibrahim, Jun Lian, Rajan Jain, Tong Zhu, Hemant Parmar, James M. Balter, Yue Cao, Eli Gibson, and Dorin Comaniciu, "The importance of data domain on self-supervised learning for brain metastasis detection and segmentation, " SPIE Medical Imaging 2023: Computer-Aided Diagnosis (Vol. 12465).
Gengyan Zhao, Youngjin Yoo, Thomas J. Re, Jyotipriya Das, Wang Hesheng, Michelle Kim, Colette Shen, Yueh Lee, Douglas Kondziolka, Mohannad Ibrahim, Jun Lian, Rajan Jain, Tong Zhu, Hemant Parmar, James M. Balter, Yue Cao, Eli Gibson, and Dorin Comaniciu, "3D-2D GAN based brain metastasis synthesis with configurable parameters for fully 3D data augmentation, " SPIE Medical Imaging 2023: Image Processing (Vol. 12464, pp. 115-120). "Oral presentation"
Ghesu, Florin C., Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Dominik Neumann, Pragneshkumar Patel, R. S. Vishwanath et al. "Contrastive self-supervised learning from 100 million medical images with optional supervision." Journal of Medical Imaging 2022 Nov 1;9(6):064503.
Gibson, Eli, Bogdan Georgescu, Pascal Ceccaldi, Pierre-Hugo Trigan, Youngjin Yoo, Jyotipriya Das, Thomas J. Re et al. "Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans." Radiology: Artificial Intelligence 4, no. 3 (2022): e210115.
Youngjin Yoo, Pascal Ceccaldi, Siqi Liu, Thomas J. Re, Yue Cao, James M. Balter, and Eli Gibson. "Evaluating deep learning methods in detecting and segmenting different sizes of brain metastases on 3D post-contrast T1-weighted images." Journal of Medical Imaging 8, no. 3 (2021): 037001.
Nael, Kambiz, Eli Gibson, Chen Yang, Pascal Ceccaldi, Youngjin Yoo, Jyotipriya Das, Amish Doshi et al. "Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks." Scientific reports 11, no. 1 (2021): 1-10.
Nguyen P. Nguyen, Youngjin Yoo, Andrei Chekkoury, Eva Eibenberger, Thomas J. Re, Jyotipriya Das, Abishek Balachandran, Yvonne W. Lui, Pina C. Sanelli, Thomas J. Schroeppel, Uttam Bodanapally, Savvas Nicolaou, Tommi A. White, Filiz Bunyak, Dorin Comaniciu, and Eli Gibson. "Brain midline shift detection and quantification by a cascaded deep network pipeline on non-contrast computed tomography scans," International Conference on Computer Vision (ICCV) AI-enabled Medical Image Analysis Workshop 2021. Accepted.
Youngjin Yoo, Pascal Ceccaldi, Siqi Liu, Thomas J. Re, Yue Cao, James M. Balter and Eli Gibson, "Evaluating deep learning methods in detecting and segmenting different sizes of brain metastases on 3D post-contrast T1-weighted images", SPIE Journal of Medical Imaging 8 (3), 037001, 2021.
Kambiz Nael, Eli Gibson, Chen Yang, Pascal Ceccaldi, Youngjin Yoo, Jyotipriya Das, Amish Doshi, Bogdan Georgescu, Nirmal Janardhanan, Benjamin Odry, Mariappan Nadar, Michael Bush, Thomas J. Re, Stefan Huwer, Sonal Josan, Heinrich von Busch, Heiko Meyer, David Mendelson, Burton P. Drayer, Dorin Comaniciu & Zahi A. Fayad. Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks. Scientific Reports ;11(1):1-0. 2021.
Eduardo J Mortani Barbosa, Bogdan Georgescu, Shikha Chaganti, Gorka Bastarrika Aleman, Jordi Broncano Cabrero, Guillaume Chabin, Thomas Flohr, Philippe Grenier, Sasa Grbic, Nakul Gupta, François Mellot, Savvas Nicolaou, Thomas Re, Pina Sanelli, Alexander W Sauter, Youngjin Yoo, Valentin Ziebandt, Dorin Comaniciu. "Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort," European Radiology. 2021 May 1:1-1.
Po-Jui Lu, Youngjin Yoo, Reza Rahmanzadeh, Riccardo Galbusera, Matthias Weigel, Pascal Ceccaldi, Thanh D Nguyen, Pascal Spincemaille, Yi Wang, Alessandro Daducci, Francesco La Rosa, Meritxell Bach Cuadra, Robin Sandkühler, Kambiz Nael, Amish Doshi, Zahi A Fayad, Jens Kuhle, Ludwig Kappos, Benjamin Odry, Philippe Cattin, Eli Gibson, Cristina Granziera. "GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology." NeuroImage: Clinical 29 (2020): 102522.
Dao, Elizabeth, Tam, Roger, Hsiung, Ging-Yuek, ten Brinke, Lisanne, Crockett, Rachel, Barha, Cindy K., Yoo, Youngjin, Al Keridy, Walid, Doherty, Stephanie H., Laule, Cornelia, MacKay, Alex L., Liu-Ambrose, Teresa, ‘Exploring the Contribution of Myelin Content in Normal Appearing White Matter to Cognitive Outcomes in Cerebral Small Vessel Disease’. Journal of Alzheimer's Disease, 1 Jan. 2021 : 1 – 11.
Florin C Ghesu, Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Eli Gibson, RS Vishwanath, Abishek Balachandran, James M Balter, Yue Cao, Ramandeep Singh, Subba R Digumarthy, Mannudeep K Kalra, Sasa Grbic, Dorin Comaniciu, "Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment," Medical Image Analysis. 2020 Oct 14:101855.
Shikha Chaganti, Philippe Grenier, Abishek Balachandran, Guillaume Chabin, Stuart Cohen, Thomas Flohr, Bogdan Georgescu, Sasa Grbic, Siqi Liu, François Mellot, Nicolas Murray, Savvas Nicolaou, William Parker, Thomas Re, Pina Sanelli, Alexander W Sauter, Zhoubing Xu, Youngjin Yoo, Valentin Ziebandt, Dorin Comaniciu, "Automated quantification of CT patterns associated with COVID-19 from chest CT." Radiology: Artificial Intelligence 2, no. 4 (2020): e200048.
Mahmoud Mostapha, Boris Mailhe, Xiao Chen, Pascal Ceccaldi, Youngjin Yoo, Mariappan Nadar, "Braided Networks for Scan-Aware MRI Brain Tissue Segmentation", IEEE International Symposium on Biomedical Imaging (ISBI), pp. 136-139, 2020.
Morris, Sarah R., Richard Davis Holmes, Adam V. Dvorak, Hanwen Liu, Youngjin Yoo, Irene M. Vavasour, Silvia Mazabel et al. "Brain myelin water fraction and diffusion tensor imaging atlases for 9‐10 year‐old children." Journal of Neuroimaging 30, no. 2 (2020): 150-160.
Oh, J., Ontaneda, D., Azevedo, C., Klawiter, E.C., Arnold, D., Bakshi, R., Calabresi, P.A., Crainiceanu, C.M., Dewey, B., Freeman, L., Gauthier, A., Henry, R., Inglese, M., Kolind, S., Li, D., Mainero, C., Menon, R., Nair, G., Narayanan, S., Nelson, F., Pelletier, D., Rauscher, A., Rooney, W., Sati, P., Schwartz, P., Shinohara, R.T., Tagge, I., Traboulsee, A., Wang, Y. Yoo, Y., Yousry, T., Zhang, Y., Sicotte, N., and Reich, D.S., on behalf of the North American Imaging in Multiple Sclerosis Cooperative. Imaging Outcome Measures for Proof-of-Concept Clinical Trials Assessing Neuroprotection and/or Repair in MS: A Consensus Statement from the NAIMS Cooperative. Neurology, 92(11):519-533, 2019.
Y. Yoo, L.Y.W. Tang, T. Brosch, D.K.B. Li, S. Kolind, I. Vavasour, A. Rauscher, A. MacKay, A. Traboulsee and R. Tam. Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls. NeuroImage: Clinical, 17:169-178, 2018.
Y. Yoo, L.Y.W. Tang, S. Kim, H. Kim, L.E. Lee, D.K.B. Li, S. Kolind, A. Traboulsee, R. Tam. Hierarchical multimodal fusion of deep-learned lesion and tissue integrity features in brain MRIs for distinguishing neuromyelitis optica from multiple sclerosis. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Part III, pages 480-488, 2017. (travel award)
A. Porisky, T. Brosch, E. Ljungberg, L.Y.W. Tang, Y. Yoo, B. De Leener, A. Traboulsee, J. Cohen-Adad, and R. Tam. Grey matter segmentation in spinal cord MRIs via 3D convolutional encoder networks with shortcut connections. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Deep Learning in Medical Image Analysis (DLMIA), pages 330-337, 2017.
Y. Yoo, L.Y.W. Tang, D.K.B. Li, L. Metz, S. Kolind, A. Traboulsee and R. Tam. Deep learning of brain lesion patterns and user-defined clinical and MRI features for predicting conversion to multiple sclerosis from clinically isolated syndrome. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 7(3): 250-259, 2017.
E. Ljungberg, I. Vavasour, R. Tam, Y. Yoo, A. Rauscher, D.K.B. Li, A. Traboulsee, A. MacKay, S. Kolind. Rapid myelin water imaging in human cervical spinal cord. Magnetic Resonance in Medicine, 78(4):1482-1487, 2016.
Y. Yoo, L.Y.W. Tang, T. Brosch, D.K.B. Li, L. Metz, A. Traboulsee, and R. Tam. Deep learning of brain lesion patterns for predicting future disease activity in patients with early symptoms of multiple sclerosis. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Deep Learning in Medical Image Analysis (DLMIA), pages 86-94, 2016. (oral presentation)
L.Y.W. Tang, T. Brosch, X. Liu, Y. Yoo, A. Traboulsee, D.K.B. Li and R. Tam. Corpus callous segmentation in brain MRIs via robust target-localization and joint supervised feature extraction and prediction. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Part II, pages 406-414, 2016.
T. Brosch, L.Y.W. Tang, Y. Yoo, D.K.B. Li, A. Traboulsee, and R. Tam. Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Transactions on Medical Imaging (Special Issue on Deep Learning in Medical Imaging), 35(5):1229-1239, 2016.
T. Brosch, Y. Yoo, L.Y.W. Tang, and R. Tam. Deep Learning of Brain Images and its Application to Multiple Sclerosis. In G. Wu, D. Shen, and M. Sabuncu, editors, Machine Learning and Medical Imaging, Chapter 3. Elsevier, 2016
T. Brosch, Y. Yoo, L.Y.W. Tang, D.K.B. Li, A. Traboulsee, and R. Tam. Deep convolutional encoder networks for multiple sclerosis lesion segmentation. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Part III, pages 3-11, 2015.
Y. Yoo, T. Prasloski, I. Vavasour, A. MacKay, A. Traboulsee, D.K.B. Li, and R. Tam. Fast computation of myelin maps from MRI T2 relaxation data using multicore CPU and graphics card parallelization. Journal of Magnetic Resonance Imaging, 41(3):700-707, 2015.
Y. Yoo, T. Brosch, A. Traboulsee, D.K.B. Li, and R. Tam. Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Machine Learning in Medical Imaging (MLMI), pages 113-120, 2014. (oral presentation)
T. Brosch, Y. Yoo, D.K.B. Li, A. Traboulsee, and R. Tam. Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Part II, pages 463-470, 2014.
Y. Yoo and R. Tam. Non-local spatial regularization of MRI T2 relaxation images for myelin water quantification. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Part I, pages 614-621, 2013.
B. Park, S. Han, W. Choe, J. Lim, Y. Yoo, and S. Lee. Low light imaging system with expanding spectrum band for digital camera. In Proceedings of IEEE International Conference on Consumer Electronics (ICCE), pages 35-36, 2012.
Y. Yoo, W. Choe, and S. Lee. Wide-band image guided visible-band image enhancement. In Proceedings of IEEE International Conference on Image Processing (ICIP), pages 3405-3408, 2011.
Y. Yoo, K. Lee, W. Choe, S. Park, and S. Lee, Chang-Yong Kim. A digital ISO expansion technique for digital cameras. In Proceedings of IS&T/SPIE Electronic Imaging, pages 75370U-75370U-12, 2010.
Y. Yoo, W. Choe, J. Kwon, S. Park, S. Lee, and C. Kim. Low-light imaging method with visible-band and wide-band image pair. In Proceedings of IEEE International Conference on Image Processing (ICIP), pages 2773-2776, 2009.
Y. Yoo, H. Wey, S. Lee, and C. Kim. Profile based fast noise estimation and high ISO noise reduction for digital cameras. In Proceedings of IS&T/SPIE Electronic Imaging, pages 68170B-68170B-12, 2008. (oral presentation)
Y. Yoo, S. Lee, W. Choe, and C. Kim. CMOS image sensor noise reduction method for image signal processor in digital cameras and camera phones. In Proceedings of IS&T/SPIE Electronic Imaging, pages 65020S-65020S-10, 2007.
Abstracts and Short Papers
Youngjin Yoo, Eli Gibson, Gengyan Zhao, Andreea E. Sandu, Thomas J. Re, Jyotipriya Das, Wang Hesheng, Michelle Kim, Colette Shen, Yueh Lee, Douglas Kondziolka, Mohannad Ibrahim, Jun Lian, Rajan Jain, Tong Zhu, Hemant Parmar, Dorin Comaniciu, James M. Balter and Yue Cao, "An automated brain metastasis detection and segmentation system from MRI with a large multi-institutional dataset, " ASTRO Annual Meeting 2023, Accepted. "Oral presentation"
P.-J. Lu, R. Rahmanzadeh, R. Galbusera, M. Weigel, Y. Yoo, P. Ceccaldi, Y. Wang, J. Kuhle, L. Kappos, P. Cattin, B. Odry, E. Gibson, C. Granziera, "Attention-based convolutional network quantifying the importance of quantitative MR metrics in the multiple sclerosis lesion classification", In Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM) Scientific Meeting, 2020.
Maxime Bertrait, Pascal Ceccaldi, Boris Mailhé, Youngjin Yoo, Mariappan S. Nadar, "3D Dual Recursive Refiner Network for Robust Segmentation: Application to Brain Extraction", In Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM) Scientific Meeting, 2020.
L.Y.W. Tang, E. Lapointe, J. Chan, Y. Yoo, L.E. Lee, A. Riddehough, S. Kolind, S. Kim, H. Kim, D.K.B. Li, A. Traboulsee, R. Tam. Machine learning for automated analysis of brain lesional patterns for NMOSD-MS differential diagnosis. In Proceedings of Canadian Association of Radiologists Annual Meetings, Artificial Intelligence in Radiology, 2018. (first place in the scientific research project abstract category)
L.Y.W. Tang, S. Garg, Y. Yoo, A. Riddehough, S. Kolind, A. Traboulsee, L. Metz, D.K.B. Li, R. Tam. Corpus callosum atrophy develops within three months since CIS trial enrolment. In Proceedings of European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), 2017.
Y. Yoo, L.Y.W. Tang, S. Kim, H. Kim, L.E. Lee, D.K.B. Li, S. Kolind, A. Traboulsee, R. Tam. Deep learning of brain MRI lesion and diffusion tensor imaging features for distinguishing neuromyelitis optica spectrum disorder from multiple sclerosis. In Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM) White Matter Disease Workshop, 2017.
L.Y.W. Tang, Y. Yoo, T. Brosch, L.E. Lee, S. Kim, H. Kim, D.K.B. Li, S. Kolind, A. Traboulsee, R. Tam. DTI measures in the corpus callosum demonstrate group differences between multiple sclerosis and neuromyelitis optical spectrum disorder. In Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM) White Matter Disease Workshop, 2017.
Y. Yoo, L.Y.W. Tang, T. Brosch, D.K.B. Li, S. Kolind, I. Vavasour, A. Rauscher, A. MacKay, A. Traboulsee, and R. Tam. Deep learning of joint myelin-T1w MRI features on normal-appearing brain tissues distinguishes multiple sclerosis from healthy controls. In Proceedings of Pan-Asian Committee for Treatment and Research in Multiple Sclerosis (PACTRIMS), 2016. (oral and poster presentation)
M. Le, A. Rauscher, T. Brosch, Y. Yoo, L. Tang, M. Jarrett, A. Traboulsee, D.K.B. Li, and R. Tam. FLAIR^2 improves automatic lesion segmentation over FLAIR in MS patients. In Proceedings of European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), 2016.
J. Schubert, E. MacMillan, I. Vavasour, D. Leppert, N. Seneca, E. Vianna, A. Dayakanchuk, Y. Yoo, R. Tam, S. Kolind, and A. Traboulsee. Myelin damage in relapsing multiple sclerosis is associated with decreased N-acetylaspartate and creatine concentrations. In Proceedings of the American Academy of Neurology (AAN) Annual Meeting, 2016.
E. Ljungberg, I. Vavasour, R. Tam, Y. Yoo, A. Rauscher, D.K.B. Li, A. Traboulsee, A. MacKay, and S. Kolind. Myelin water imaging in human cervical spinal cord using a 3D-GRASE sequence. In Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM) Scientific Meeting, 2016.
E. MacMillan, J. Schubert, I. Vavasour, E. Vianna, A. Dzyakanchuk, Y. Yoo, R. Tam, S. Kolind, and A. Traboulsee. N-acetylaspartate and creatine decrease with myelin damage in relapsing multiple sclerosis. In Proceedings of European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), 2015.
Invited Talks and Meeting Presentations
Machine learning of brain MR images and its applications to multiple sclerosis. Citywide Round Seminar Series, Faculty of Medicine, University of British Columbia, Vancouver, Canada, Dec. 2016.
Deep learning of joint myelin-T1w MRI features on normal-appearing brain tissues distinguishes multiple sclerosis from healthy controls. North American Imaging in Multiple Sclerosis (NAIMS) Meeting, Toronto, Canada, Nov. 2016.
Machine learning for predicting the risk of conversion to the 2005 McDonald criteria in patients with early symptoms of multiple sclerosis. North American Imaging in Multiple Sclerosis (NAIMS) Meeting, Toronto, Canada, Nov. 2016.
Patents
Youngjin Yoo, Eli Gibson, Gengyan Zhao, Long Xie, Boris Mailhe, Dorin Comaniciu and Thomas Re. "A high-resolution 3D brain MRI generative system for pathological aging monitoring and neuro-degenerative abnormality detection." U.S. Patent Application 2023E17533 US 2023.
Youngjin Yoo, Eli Gibson, Pragneshkumar Patel, Gianluca Paladini, Poikavila Ullaskrishnan and Dorin Comaniciu. "Adaptive aggregation for federated learning.'' U.S. Patent Application 17449165, filed March 30, 2023.
Youngjin Yoo, Gianluca Paladini, Eli Gibson, Pragneshkumar Patel, and Poikavila Ullaskrishnan. "Privacy-preserving data curation for federated learning.'' U.S. Patent Application 17/449,190, filed March 30, 2023.
Youngjin Yoo, Thomas Re, Eli Gibson, Andrei Chekkoury. Automatic hemorrhage expansion detection from head CT images. US Patent 17211927
Eli Gibson, Bogdan Georgescu, Pascal Ceccaldi, Youngjin Yoo, Jyotipriya Das, Thomas Re, Eva Eibenberger, Andrei Chekkoury, Barbara Brehm, Thomas Flohr, Dorin Comaniciu, Pierre-Hugo Trigan. Machine learning for automatic detection of intracranial hemorrhages with uncertainty measures from ct images. US Patent 17249783
Y. Yoo, P. Cecadilli and E. Gibson. Automatic detection of lesions in medical images using 2D and 3D deep learning networks. US Patent 17118668
Nguyen Nguyen, Y. Yoo, P. Cecadilli and E. Gibson. Automated estimation of midline shift in brain CT images. US Patent 17303932
Mostapha, Mahmoud, Boris Mailhe, Mariappan S. Nadar, Pascal Ceccaldi, and Youngjin Yoo. "Protocol-Aware Tissue Segmentation in Medical Imaging." U.S. Patent Application 15/929,430, filed April 1, 2021.
Y. Yoo, P. Cecadilli, E. Gibson and M. S. Nadar. Saliency mapping by feature map reduction and perturbation modeling. US patent pending, US Patent App. 16707209, 2019.
Y. Yoo and M. S. Nadar. Automated uncertainty estimation of lesion segmentation. US Patent App. 16/355,881, 2018.
W. Choe, S. Lee, and Y. Yoo. Image sensor and method using near infrared signal. US Patent 9,435,922, 2016.
Y. Yoo, H. Wey, and S. Lee. Noise-reduction method and apparatus. US Patent 9,014,503, 2015.
Y. Yoo, W. Choe, and S. Lee. Method and apparatus for reducing noise. US Patent 8,768,093, 2014.
J. Lim, W. Choe, B. Park, Y. Yoo, and S. Lee. Apparatus and method for processing images. US Patent 8,693,770, 2014.
Y. Yoo, W. Choe, S. Lee, and K. Lee. Apparatus and method for generating high ISO image. US Patent 8,471,928, 2013.
W. Choe, Y. Yoo, and S. Lee. Image processing apparatus and method of noise reduction. US Patent 8,614,746, 2013.
Y. Moon, S. Lee, H. Lee, and Y. Yoo. Method and apparatus for simultaneously reducing various kinds of noises from image. US Patent 8,548,234, 2013.
Y. Yoo, and H. Wey. Apparatus and method for reducing noise from an image. US Patent 8,478,058, 2013.
Y. Yoo, and S. Lee. Noise reduction method, medium, and system. US Patent 8,467,003, 2013.
Y. Yoo, W. Choe, and J. Kwon. Apparatus and method for processing image. US Patent 8,463,069, 2013.
K. Lee, S. Kim, S. Lee, W. Choe, and Y. Yoo. Apparatus and method for generating high sensitivity images in dark environment. US Patent 8,406,560, 2013.
H. Oh, W. Choe, S. Lee, H. Song, S. Park, Y. Yoo, J. Kwon, and K. Lee. Apparatus and method for obtaining motion adaptive high dynamic range image. US Patent 8,379,094, 2013.
Y. Yoo, W. Choe, J. Lim, and S. Lee. Noise reduction apparatus having edge enhancement function and method thereof. US Patent 8,335,395, 2012.
H. Wey, H. Oh, Y. Yoo, and S. Lee. Method and system for processing low-illuminance image. US Patent 8,295,629, 2012.
J. Kwon, S. Lee, D. Chung, W. Choe, and Y. Yoo. Method and apparatus for enhancing image, and image-processing system using the same. US Patent 8,023,763, 2011.
Y. Yoo, H. Lee, D. Park, S. Lee, and S. Lee. Method and apparatus for reducing noise of image sensor. US Patent 8,004,586, 2011.
Y. Yoo, S. Lee, and H. Ok. Apparatus and method for reducing noise of image sensor. US Patent 7,813,583, 2010.
Y. Yoo, and S. Lee. Method and apparatus for estimating noise determination criteria in an image sensor. US Patent 7,734,060, 2010.
Theses
Deep learning for feature discovery in brain MRIs for patient-level classification with applications to multiple sclerosis. Doctoral thesis, The University of British Columbia, 2018.
Robust and fast T2 decay analysis for measuring myelin water in MRI. Master's thesis, The University of British Columbia, 2013. (The developed software is being used for a number of key clinical trials of new multiple sclerosis therapies at UBC's Multiple Sclerosis/MRI Research Group. Most notably, the software was used to show a beneficial treatment effect of Ocrelizumab and Alemtuzumab on myelin repair in separate trials.)