[2019]

JOURNALS

  • Interpretable AD Diagnostic Model [Link]

E. Lee*, J.-S. Choi*, M. Kim, and H.-I. SUK, “Toward an Interpretable Alzheimer’s Disease Diagnostic Model with Regional Abnormality Representation via Deep Learning,” NeuroImage, 2019 (2018-JCR-IF: 5.812, NEUROIMAGING: 1/14, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 11/129) (* Equally contributed)

 
 
 

BC Kim*, JS Yoon*, J.-S. Choi, and H.-I. SUK, “Multi-Scale Gradual Integration CNN for False Positive Reduction in Pulmonary Nodule Detection,” Neural Networks, Vol. 115, pp. 1-10, July 2019 (2017-JCR-IF: 7.197, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 7/132, NEUROSCIENCES: 19/261) (* Equally contributed)

 

  • Multi-Modal Alzheimer’s Disease Diagnosis [Link]

Y. Shi, H.-I. SUK, Y. Gao, S.-W. Lee, and D. Shen, “Leveraging Coupled Interaction for Multi-Modal Alzheimer’s Disease Diagnosis,” IEEE Transactions on Neural Networks and Learning Systems, February 2019(2016-JCR-IF: 7.982, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 6/132, COMPUTER SCIENCE, THEORY & METHODS: 2/103, ENGINEERING, ELECTRICAL & ELECTRONIC: 8/260)

  • Modeling Functional Dynamics in fMRI [Link]

E. Jun, E. Kang, J. Choi, and H.-I. SUK, “Modeling Regional Dynamics in Low-Frequency Fluctuation and Its Application to Autism Spectrum Disorder Diagnosis,” NeuroImage, Vol. 184, pp. 669-686, January 2019 (2016-JCR-IF: 5.426, NEUROIMAGING: 1/14, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 13/128)

CONFERENCES

  • E. Jeon, W. Ko, JS Yoon, and H.-I. SUK, “Toward Subject Invariant and Class Disentangled Representation in BCI via Cross-Domain Mutual Information Estimator,” arXiv, 1910.07747, 2019.

  • W. Jung, AW 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), Shenzhen, China, October 13-17, 2019.

  • JS Yoon, W. Ko, and H.-I. SUK, “Plug-in Factorization for Latent Representation Disentanglement,” arXiv, 1905.11088, 2019.

  • E. Jun*, AW Mulyadi*, and H.-I. SUK, “Stochastic Imputation and Uncertainty-Aware Attention to EHR for Mortality Prediction,” The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 14-19, 2019. (Oral Presentation) (* Equally contributed)

  • E. Jun, J. Lee, and H.-I. SUK, “Graph convolutional network with sparse representation in resting-state fMRI for MDD identification,” Organization for Human Brain Mapping (OHBM), Rome, Italy, June 9-13, 2019.

  • W. Ko, E. Jeon, and H.-I. SUK, “Semi-Supervised Deep Adversarial Learning for Brain-Computer Interface,” Proc. 7th IEEE International Winter Conference on Brain-Computer Interface, Gangwon, Korea, Feb. 18-20, 2019.

  • E. Jeon, W. Ko, and H.-I. SUK, “Domain Adaptation with Source Selection for Motor-Imagery based BCI,” Proc. 7th IEEE International Winter Conference on Brain-Computer Interface, Gangwon, Korea, Feb. 18-20, 2019.


[2018]

JOURNALS

  • Pulmonary Nodule Detection in CT [Link]

B.-C. Kim, J.-S. Choi, and H.-I. SUK, “Multi-Scale Gradual Integration CNN for False Positive Reduction in Pulmonary Nodule Detection,” arXiv, 1807.10581, 2018

 
 

  • Group Sparse Regression for Neuroimaging Genetic Study

X. Zhu, H.-I. SUK, and D. Shen, “Group Sparse Reduced Rank Regression for Neuroimaging Genetic Study,”  World Wide Web – Internet and Web Information Systems, 2018 (2017-JCR-IF: 1.15)

  • BCI-based Wheelchair Control [Link]

K.-T. Kim, H.-I. SUK, and S.-W. Lee,  “Commanding a Brain-Controlled Wheelchair using Steady-State Somatosensory Evoked Potentials,”  IEEE Transactions on Neural Systems and Rehabilitation Engineering, March 2018. (2017-JCR-IF: 3.972, REHABILITATION: 3/65)

CONFERENCES

  • E.-S. Kang and H.-I. SUK, “Probabilistic Source Separation on resting-state fMRI and Its Use for Early MCI Identification,” Proc of 2018 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Granada, Spain, September 16-20, 2018.

  • J.-S. Choi, E. Lee, and H.-I. SUK, “Regional Abnormality Representation Learning in Structural MRI for AD/MCI Diagnosis,” Proc of 2018 International Conference on Machine Learning in Medical Imaging (MLMI), Granada, Spain, LNCS 11046, pp. 64-72, September 15, 2018.

  • W. Ko, J. Yoon, and H.-I. SUK, “Towards Reducing Calibration in BCI: Artificial EEGs Generation by Deep Learning,” Proc. of 7th International BCI Meeting 2018, Pacific Grove, USA, May 21-25, 2018.

  • W. Ko, J. Yoon, E. Kang, E. Jun, J.-S. Choi, and H.-I. SUK, “Deep Recurrent Spatio-Spectral Neural Network for Motor Imagery based BCI,” Proc. of IEEE 6th International Winter Workshop on Brain-Computer Interface, High1 Resort, Korea, January 15-17, 2018.


[2017]

JOURNALS

  • Multiple Functional Networks for ASD diagnosis [Link]

T.-E. Kam, H.-I. SUK, and S.-W. Lee, “Multiple Functional Networks Modeling for Autism Spectrum Disorder Diagnosis,” Human Brain Mapping, Vol. 38, No. 11, pp. 5804-5821, November 2017 (2016-JCR-IF: 4.530, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 12/126)

  • Genetic Biomarker Selection [Link]

X. Zhu, H.-I. SUK, H. Meng, and D. Shen, “Low-Rank Graph-Regularized Structured Sparse Regression for Identifying Genetic Biomarkers,” IEEE Transactions on Big Data, Vol. 3, No. 4, pp. 405-414, December 2017

  • Discriminative Self-Representation [Link]

X. Zhu, H.-I. SUK, S.-W. Lee, and D. Shen, “Discriminative Self-representation Sparse Regression for Neuroimaging-based Alzheimer’s Disease Diagnosis,” Brain Imaging and Behavior, pp. 1-14, June 2017 (2016-JCR-IF: 3.985, NEUROIMAGING: 4/14)

  • Person Identification with EEG [Link]

B.-K. Min, H.-I. SUK, M.-H. Ahn, M.-H. Lee, and S.-W. Lee, “Individual Identification using Cognitive Electroencephalographic Neurodynamics,” IEEE Transactions on Information Forensics and Security, Vol. 12, No. 9, pp. 2159-2167, September 2017 (2015-JCR-IF: 2.441, COMPUTER SCIENCE, THEORY & METHODS: 10/105)

  • Relational Regularization for Feature Selection [Link]

X. Zhu, H.-I. SUK, L. Wang, S.-W. Lee, and D. Shen, “A Novel Relational Regularization Feature Selection Method for Joint Regression and Classification in AD Diagnosis,” Medical Image Analysis, Vol. 38, pp. 205-214, May 2017. (2015-JCR-IF: 4.565, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 8/130, COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS: 4/104, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 11/124. ENGINEERING, BIOMEDICAL: 7/76)

  • Deep Ensemble Learning of Sparse Regression Models [Link]

H.-I. SUK, S.-W. Lee, and D. Shen, “Deep Ensemble Learning of Sparse Regression Models for Brain Disease Diagnosis,” Medical Image Analysis, Vol. 37, pp. 101-113, April 2017 (2015-JCR-IF: 4.565, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 8/130, COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS: 4/104, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 11/124. ENGINEERING, BIOMEDICAL: 7/76)

  • Deep Learning in Medical Imaging [Link]

D. Shen*, G. Wu, and H.-I. SUK*, “Deep Learning in Medical Image Analysis,” Annual Review of Biomedical Engineering, Vol. 19, pp. 221-248, June 2017. (*Co-first and Co-corresponding Authors, 2016-JCR-IF: 10.514, ENGINEERING, BIOMEDICAL: 1/77)

CONFERENCES

  • J.-S. Yoon and H.-I. SUK, “Gated Two-Stage Convolutional Neural Network for Ischemic Stroke Lesion Segmentation,” Proc. of 2017 International Workshop on Ischemic Stroke Lesion Segmentation Challenge (ISLES) 2017 (in conjunction with MICCAI), Quebec, Canada, September 14, 2017.

  • E.-J. Jun and H.-I. SUK, “Region-Wise Stochastic Pattern Modeling for Autism Spectrum Disorder Identification and Temporal Dynamics Analysis,” Proc. of 2017 International Workshop on Connectomics in NeuroImaging (CNI) (in conjunction with MICCAI), Quebec, Canada, September 14, 2017.


[2016]

JOURNALS

  • Canonical Feature Selection for AD Diagnosis

X. Zhu, H.-I. SUK, and D. Shen, “Canonical Feature Selection for Joint Regression and Multi-class Identification in Alzheimer’s Disease Diagnosis,” Brain Imaging and Behavior, Vol. 10, No. 3, pp. 818-828, September 2016. (2014-JCR-IF: 4.598, NEUROIMAGING: 3/14)

  • Movement Intention Decoding from EMG signals

K.-H. Park, H.-I. SUK, and S.-W. Lee,  “Position-Independent Decoding of Movement Intention for Proportional Myoelectric Interfaces,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 24, No. 9, pp. 928-939, September 2016. (2014-JCR-IF: 3.188, REHABILITATION: 3/64)

  • Sparse Multi-Response Tensor Regression

Z. Li, H.-I. SUK, Dinggang Shen, Lexin Li, “Sparse Multi-Response Tensor Regression for Alzheimer’s Disease Study with Multivariate Clinical Assessments,” IEEE Transactions on Medical Imaging, Vol. 35, No. 8, pp. 1927-1936, August 2016. (2014-JCR-IF: 3.756, COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS: 9/104, ENGINEERING, ELECTRICAL & ELECTRONIC: 13/255, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 17/124)

  • Functional Dynamics Modelling in Resting-State fMRI

H.-I. SUK, C.-Y. Wee, S.-W. Lee, and D. Shen, “State-Space Model with Deep Learning for Functional Dynamics Estimation in Resting-State fMRI,” NeuroImage, Vol. 129, pp. 292-307, April 2016. (2014-JCR-IF: 6.357, NEUROIMAGING: 1/14, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 3/125)

  • Subspace Regularized Sparse Model for AD Diagnosis

X. Zhu, H.-I. SUK, S.-W. Lee, and D. Shen, “Subspace Regularized Sparse Multi-Task Learning for Multi-Class Neurodegenerative Disease Identification,” IEEE Transactions on Biomedical Engineering, Vol. 63, No. 3, pp. 607-618, 2016. (2014-JCR-IF: 2.347, BIOMEDICAL ENGINEERING: 28/76)

  • Deep Sparse Multi-Task Learning for AD Diagnosis

H.-I. SUK, S.-W. Lee, and D. Shen, “Deep Sparse Multi-Task Learning for Feature Selection in Alzheimer’s Disease Diagnosis,” Brain Structure & Function, Vol. 221, No. 5, pp. 2569-2587, June 2016. (2015-JCR-IF: 5.811, ANATOMY & MORPHOLOGY: 1/21)

CONFERENCES

  • H.-I. SUK and D. Shen, “Deep Ensemble Sparse Regression Network for Alzheimer’s Disease Diagnosis,” Proc. of 2016 International Workshop on Machine Learning in Medical Imaging (MLMI), Athens, Greece, LNCS 10019, pp. 113-121, October 16, 2016.

  • X. Zhu, H.-I. SUK, H. Huang, and D. Shen, “Structured Sparse Low-Rank Regression Model for Brain-Wide and Genome-Wide Associations,” Proc. of 2016 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Athens, Greece, Part I, LNCS 9900, pp. 344-353, October 17-21, 2016. (Poster, Acceptance Ratio=30.1%)

  • X. Zhu, H.-I. SUK, K.-H. Thung, Y. Zhu, G. Wu, and D. Shen, “Joint Discriminative and Representative Feature Selection for Alzheimer’s Disease Diagnosis”, Proc. of 2016 International Workshop on Machine Learning in Medical Imaging (MLMI), Athens, Greece, LNCS 10019, pp. 77-85, October 16, 2016.

  • B. Kim, Y. Sung, and H.-I. SUK, “Deep Feature Learning for Pulmonary Nodule Classification in a Lung CT,” Proc. of 4th International Winter Conference on Brain-Computer Interface, Yongpyong Resort, Korea, Feb. 23-24, 2016.

  • E.-S. Kang, B. Kim, and H.-I. SUK, “An Empirical Suggestion for Collaborative Learning in Motor Imagery-based BCIs,” Proc. of 4th International Winter Conference on Brain-Computer Interface, Yongpyong Resort, Korea, Feb. 23-24, 2016.


[2015]

JOURNALS

  • Sparse Learning for MRI-based IQ Estimation

L. Wang, C.-Y. Wee, H.-I. SUK, X. Tang, and D. Shen, “MRI-based Intelligence Quotient (IQ) Estimation with Sparse Learning”, PLoS One, Vol. 10, No. 3, pp. e0117295,  March 2015. (2013-JCR-IF: 3.534, MULTIDISCIPLINARY SCIENCES: 8/55)

  • Multimodal Manifold-Regularized Transfer Learning

B. Cheng, M. Liu, H.-I. SUK, D. Shen, and D. Zhang, “Multimodal Manifold-Regularized Transfer Learning for MCI Conversion Prediction”, Brain Imaging and Behavior, Vol. 9, No. 4, pp. 913-926, December 2015. (2014-JCR-IF: 4.598, NEUROIMAGING: 3/14)

  • Discriminative Sparse Functional Connectivity Estimation

H.-I. SUK, C.-Y. Wee, S.-W. Lee, and D. Shen, “Supervised Discriminative Group Sparse Representation for Mild Cognitive Impairment Diagnosis,” Neuroinformatics, Vol. 13, No. 3, pp. 277-295, July 2015. (2013-JCR-IF: 3.102, COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS: 12/102)

  • Abnormal Behaviour Detection in Crowd Scenes

D.-G. Lee, H.-I. SUK, and S.-W. Lee, “Motion Influence Map for Unusual Human Activity Detection and Localization in Crowded Scenes,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 25, No. 10, pp. 1612-1623, October 2015. (2013-JCR-IF: 2.259, ENGINEERING, ELECTRICAL & ELECTRONIC: 50/248)

  • Volumetric Spatial Feature Representation

S.-S. Cho, A.-R. Lee, H.-I. SUK, J.-S. Park, and S.-W. Lee, “Volumetric Spatial Feature Representation for View-Invariant Human Action Recognition using a Depth Camera,” Optical Engineering, Vol. 54, No. 3, pp. 033102, March 2015. (2013-JCR-IF: 0.88, OPTICS: 55/82)

  • Latent Feature Representation and Multi-Modal Fusion

H.-I. SUK, S.-W. Lee, and D. Shen, “Latent Feature Representation with Stacked Auto-Encoder for AD/MCI Diagnosis,” Brain Structure & Function, Vol. 220, No. 2, pp. 841-859, March 2015. (2012-JCR-IF: 7.837, NEUROSCIENCES: 18/252, ANATOMY & MORPHOLOGY: 1/21)

CONFERENCES

  • X. Zhu, H.-I. SUK, Y. Zhu, K. Thung, G. Wu, and D. Shen, “Multi-View Classification for Identification of Alzheimer’s Disease,” Proc. of 2015 International Workshop on Machine Learning in Medical Imaging (MLMI), Munich, Germany, October 5, 2015.

  • H.-I. SUK, S.-W. Lee, and D. Shen, “A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI,” Proc. of 2015 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany, Part I, LNCS 9349, pp. 573–580, October 5-9, 2015. (Poster, Acceptance Ratio=32.5%)

  • J.-W. Kim, H.-I. SUK, J.-P. Kim, and S.-W. Lee, “Combined Regression and Classification Approach for Prediction of Driver’s Braking Intention,” Proc. IEEE 3rd International Winter Conference on Brain-Computer Interface, High1 Resort, Korea,  Jan. 12-14, 2015. (Oral)


[2014]

JOURNALS

  • Multi-Modal Feature Representation

H.-I. SUK, S.-W. Lee, and D. Shen, “Hierarchical Feature Representation and Multi-Modal Fusion with Deep Learning for AD/MCI Diagnosis,” NeuroImage, Vol. 101, pp. 569-582, November 2014. (2012-JCR-IF: 6.252, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 3/120, NEUROSCIENCE, 26/252, NEUROIMAGING: 2/14)

  • Matrix-Similarity-based Loss Function

X. Zhu, H.-I. SUK, and D. Shen, “A Novel Matrix-Similarity Based Loss for Joint Regression and Classification in AD Diagnosis,” NeuroImage, Vol. 100, pp. 91-105, October 2014. (2012-JCR-IF: 6.252, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 3/120, NEUROSCIENCE, 26/252, NEUROIMAGING: 2/14)

  • Clustering-Induced Multi-Task Learning

H.-I. SUK, S.-W. Lee, and D. Shen, “Subclass-Based Multi-Task Learning for Alzheimer’s Disease Diagnosis,” Frontiers in Aging Neuroscience, Vol. 6, No. 168, pp. 1-12, August 2014. (2012-JCR-IF: 5.224, GERIATRICS & GERONTOLOGY: 5/47, NEUROSCIENCE, 40/252)

  • Bayesian BCI Predictor

H.-I. SUK, S. Fazli, J. Mehnert, K.-R. Müller, and S.-W. Lee, “Predicting BCI Subject Performance Using Probabilistic Spatio-Temporal Filters,” PLoS One, Vol. 9, No. 2, e87056, February 2014. (2012-JCR-IF: 3.730, MULTIDISCIPLINARY SCIENCES: 7/56)

CONFERENCES

  • H.-I. SUK and D. Shen, “Clustering-Induced Multi-Task Learning for AD/MCI Classification”, Proc. of 2014 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Boston, USA, Part III, LNCS 8675, pp. 393-400, September 14-18, 2014. (Poster, Acceptance Ratio=29.35%)

  • X. Zhu, H.-I. SUK, and D. Shen, “A Novel Multi-Relation Regularization Method for Regression and Classification in AD Diagnosis”, Proc. of 2014 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Boston, USA, Part III, LNCS 8675, pp. 401-408, September 14-18, 2014. (Poster, Acceptance Ratio=29.35%)

  • R. Li, W. Zhang, H.-I. SUK, L. Wang, J. Li, D. Shen, and S. Ji, “Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis”, Proc. of 2014 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Boston, USA, Part III, LNCS 8675, pp. 305-312, September 14-18, 2014. (Poster, Acceptance Ratio=29.35%)

  • X. Zhu, H.-I. SUK, and D. Shen, “Multi-Modality Canonical Feature Selection for Alzheimer’s Disease Diagnosis,” Proc. of 2014 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Boston, USA, Part II, LNCS 8674, pp. 162–169, September 14-18, 2014. (Poster, Acceptance Ratio=29.35%)

  • X. Wang, L. Wang, H.-I. SUK, and D. Shen, “Online Discriminative Multi-Atlas Learning for Isointense Infant Brain Segmentation,” Proc. of 2014 International Workshop on Machine Learning in Medical Imaging (MLMI), Boston, USA, LNCS 8679, pp. 297–305, September 14, 2014.

  • X. Zhu, H.-I. SUK, and D. Shen, “Sparse Discriminative Feature Selection for Multi-Class Alzheimer’s Disease Classification,” Proc. of 2014 International Workshop on Machine Learning in Medical Imaging (MLMI), Boston, USA, LNCS 8679, pp. 157–164, September 14, 2014.

  • Y. Shi, H.-I. SUK, Y. Gao, and D. Shen, “Joint Coupled-Feature Representation and Coupled Boosting for AD Diagnosis,” Proc. of 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, USA, pp. 2721-2728, June 24-27, 2014. (Oral, Acceptance Ratio=5.75%)

  • X. Zhu, H.-I. SUK, and D. Shen, “Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer’s Disease Diagnosis,” Proc. of 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, USA, pp. 3089-3096, June 24-27, 2014. (Poster, Acceptance Ratio=29.88%)

  • A.-R. Lee, H.-I. SUK, and S.-W. Lee, “View-Invariant 3D Action Recognition using Spatiotemporal Self-Similarities from Depth Camera,” Proc. of 2014 International Conference on Pattern Recognition (ICPR), Stockholm, Sweden, pp. 501-505, August 24-28, 2014.


[2013]

JOURNALS

  • Bayesian Spatio-Spectral Filter Optimization for BCI

H.-I. SUK and S.-W. Lee, “A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 2, pp. 286-299, February 2013. (2011-JCR-IF: 4.908, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 1/111, ELECTRICAL & ELECTRONIC ENGINEERING: 6/245)

  • EEG-based Person Authentication

S.-K. Yeom, H.-I. SUK, and S.-W. Lee, “Person Authentication from Neural Activity of Face-Specific Visual Self-Representation,” Pattern Recognition, Vol. 46, No. 4,  pp. 1159-1169, April 2013. (2011-JCR-IF: 2.292, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 18/111, ENGINEERING, ELECTRICAL & ELECTRONIC: 35/245)

  • Non-homogeneous Spatial Filter Learning

T.-E. Kam, H.-I. SUK, and S.-W. Lee, “Non-Homogeneous Spatial Filter Optimization for EEG-Based Motor Imagery Classification,” Neurocomputing, Vol. 108, pp. 58-68, May 2013. (2011-JCR-IF: 1.580, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 39/111)

  • Incremental Sparse Gaussian Process Regression

H.-I. SUK, Y. Wang, and S.-W. Lee, “Incremental Sparse Pseudo-input Gaussian Process Regression,” International Journal of Pattern Recognition and Artificial Intelligence, Vol. 26, No. 8, pp. 1250019, January 2013. (2011-JCR-IF: 0.624)

CONFERENCES

  • D.-G. Lee, H.-I. SUK, and S.-W. Lee, “Crowd Behavior Representation Using Motion Influence Matrix for Anomaly Detection,” Proc. of 2nd IAPR Asian Conference on Pattern Recognition (ACPR), Okinawa, Japan, November 5-8, 2013. (Oral Presentation)

  • H.-I. SUK, C.-Y. Wee, and D. Shen, “Discriminative Group Sparse Representation for Mild Cognitive Impairment Classification,” Proc. of 2013 International Workshop on Machine Learning in Medical Imaging (MLMI), Nagoya, Japan, LNCS, Vol. 8184, pp. 131-138, September 22, 2013. (Poster)

  • M. Liu, H.-I. SUK, and D. Shen, “Multi-task Sparse Classifier for Diagnosis of MCI Conversion to AD with Longitudinal MR images,” Proc. of 2013 International Workshop on Machine Learning in Medical Imaging (MLMI), Nagoya, Japan, LNCS, Vol. 8184, pp. 243-250, September 22, 2013. (Poster)

  • B. Jie, D. Zhang, C.-Y. Wee, H.-I. SUK, and D. Shen, “Integrating Multiple Network Properties for MCI Identification,” Proc. of 2013 International Workshop on Machine Learning in Medical Imaging (MLMI), Nagoya, Japan, LNCS, Vol. 8184, pp. 9-16, September 22, 2013. (Poster)

  • H.-I. SUK and D. Shen, “Deep Learning-based Feature Representation for AD/MCI Classification,” Proc. of 2013 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nagoya, Japan, LNCS, Vol. 8150, pp. 583-590, September 22-26, 2013. (Poster, Acceptance Ratio=32.8%)

  • F. Liu, H.-I. SUK, C.-Y. Wee, H. Chen, and D. Shen, “High-Order Graph Matching based Feature Selection for Alzheimer’s Disease Identification,” Proc. of 2013 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nagoya, Japan, LNCS, Vol. 8150, pp. 311-318, September 22-26, 2013. (Poster, Acceptance Ratio=32.8%)


[2012]

JOURNALS

  • View-Invariant Pose Estimation

D.-C. Hur, H.-I. SUK, C. Wallraven, and S.-W. Lee, “Biased Manifold Learning for View Invariant Body Pose Estimation,” International Journal of Wavelets, Multiresolution and Information Processing, Vol. 10, No. 6, pp. 1250058, December 2012.

  • Incremental Learning in Conditional Random Field

H.-D. Yang, H.-I. SUK, and S.-W. Lee, “Accelerating Generalized Iterative Scaling Based on Staggered Aitken Method for On-line Conditional Random Fields,” International Journal of Wavelets Multiresolution and Information Processing, Vol. 10, No. 6, pp. 1250059, December 2012.

CONFERENCES

  • H.-I. SUK, Y. Wang, and S.-W. Lee, “Online Learning of Sparse Pseudo-Input Gaussian Process,” Proc. of 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Seoul, Korea, pp. 1357-1360, October 14-17, 2012. (Poster)

  • H.-I. SUK and S.-W. Lee, “A Bayesian Framework for Single-Trial EEG Classification in Brain-Computer Interface,” Proc. of 4th International Symposium on Brain and Cognitive Engineering, Seoul, Seoul, Korea, pp. 2-3, May 31, 2012. (Poster, Best Poster Award)


[~2011]

JOURNALS

  • [2011] Network of Dynamic Probabilistic Models

H.-I. SUK, A. K. Jain, and S.-W. Lee, “A Network of Dynamic Probabilistic Models for Human Interaction Analysis,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 21, No. 7, pp. 932-945, July 2011. (2009-JCR-IF: 2.548, ELECTRICAL & ELECTRONIC ENGINEERING: 24/246)

  • [2011] Frequency Bands Selection in BCI

H.-I. SUK and S.-W. Lee, “Subject and Class-Specific Frequency Bands Selection for Multi-Class Motor Imagery Classification,” International Journal of Imaging Systems and Technology – Neuroimaging and Brain-Mapping, Vol. 21, No. 2, pp. 121-122, June 2011. (2011-JCR-IF: 0.779)

  • [2010] Continuous Hand Gestures Recognition

H.-I. SUK, B.-K. Sin, and S.-W. Lee, “Hand Gesture Recognition based on Dynamic Bayesian Network,” Pattern Recognition, Vol. 43, No. 9, pp. 3059-3072, September 2010. (2009-JCR-IF: 2.554, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 21/103, ELECTRICAL & ELECTRONIC ENGINEERING: 23/246)

CONFERENCES

  • S.-K. Yeom, H.-I. SUK, and S.-W. Lee, “EEG-based Person Authentication using Face-Specific Self-Representation,” Proc. of 11th International Workshop on Neurobiology and Neuroinformatics, Okinawa, Japan, December 18-19, 2011. (Oral Presentation)

  • H.-I. SUK and S.-W. Lee, “A Probabilistic Approach for Spatio-Spectral Filters Optimization in Brain-Computer Interface,” Proc. of 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Anchorage, USA, pp. 19-24, October 9-12, 2011. (Oral Presentation)

  • H.-I. SUK and S.-W. Lee, “Data-Driven Frequency Bands Selection in EEG-based Brain-Computer Interface,” Proc. of 2011 IEEE International Workshop on Pattern Recognition in NeuroImaging (PRNI), Seoul, Korea, pp. 25-28, May 16-18, 2011. (Oral Presentation)

  • H.-I. SUK and S.-W. Lee, “Two-Layer Hidden Markov Model for Multi-Class Motor Imagery Classification,” Proc. of 1st ICPR Workshop on Brain Decoding, Istanbul, Turkey, August 22, pp. 5-8, 2010. (Oral Presentation)

  • H.-I. SUK, B.-K. Sin, and S.-W. Lee, “Analyzing Human Interactions with a Network of Dynamic Probabilistic Models,” Proc. of 2009 IEEE Workshop on Applications of Computer Vision (WACV), Snowbird, USA, Dec. 7-9, 2009. (Poster)

  • H.-I. SUK, J.-H. Lee, and S.-W. Lee, “Real-Time Tracking and Reconstruction of 3D Hand Poses for Virtual Keypad System,” Proc. of 3rd Korea-Japan Joint Workshop on Pattern Recognition (KJPR), Seoul, Korea, November 13-14, 2008. (Oral Presentation)

  • H.-I. SUK, B.-K. Sin, and S.-W. Lee, “Robust Modeling and Recognition of Hand Gestures with Dynamic Bayesian Network,” Proc. of 19th International Conference on Pattern Recognition (ICPR), Tampa, USA, pp. 1-4, December 8-11, 2008. (Poster)

  • H.-I. SUK, S.-S. Cho, H.-D. Yang, M.-C. Roh, and S.-W. Lee, “Real-Time Human-Robot Interaction Based on Continuous Gesture Spotting and Recognition,” Proc. of 39th International Symposium on Robotics, Seoul, Korea, pp. 120-123, October 15-17, 2008. (Oral Presentation)

  • H.-I. SUK, B.-K. Sin, and S.-W. Lee, “Recognizing Hand Gestures using Dynamic Bayesian Network,” Proc. of 8th IEEE International Conference on Automatic Face and Gesture Recognition (FGR), Amsterdam, The Netherlands, September 17-19, 2008. (Poster)

  • H.-I. SUK and B.-K. Sin, “Hands Gesture Recognition Based on Dynamic Bayesian Network Framework,” Proc. of 2nd Korea-Japan Joint Workshop on Pattern Recognition (KJPR), Matsushima, Japan, pp. 105-110, October 25-26, 2007. (Oral Presentation)

  • H.-I. SUK and B.-K. Sin, “Gait Recognition Using Cyclic HMMs,” Proc. of 1st Korea-Japan Joint Workshop on Pattern Recognition (KJPR), Jeju, Korea, pp. 79-84, November 23-24, 2006. (Oral Presentation)

  • H.-I. SUK and B.-K. Sin, “HMM-Based Gait Recognition with Human Profiles,” Proc. of 2006 Joint IAPR International Workshops SSPR&SPR, Hong Kong, China, LNCS, Vol. 4109, pp. 596-603, August 17-19, 2006. (Oral Presentation)