[2018]

  • 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. (Accepted)

  • 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]

  • 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]

  • 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]

  • 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]

  • 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]

  • 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 Selectionfor 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]

  • 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]

  • 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)