International Journals/Conferences – Machine Intelligence Laboratory

arXiv

K. Oh, D.-W. Heo, A. W. Mulyadi, W. Jung, E. Kang, K.H. Lee, and H.-I. Suk, “Deep Counterfactual-Guided MRI Feature Representation and Quantitatively Interpretable Alzheimer’s Disease Prediction,” Research Square, 2022. [code]/[paper]
A. W. Mulyadi, W. Jung, K. Oh, J.S. Yoon, and H.-I. Suk, “XADLiME: eXplainable Alzheimer’s Disease Likelihood Map Estimation via Clinically-guided Prototype Learning,” arXiv preprint arXiv:2207.13223, 2022. [code]/[paper]
S. Jeong, W. Ko, A. W. Mulyadi, and H.-I. Suk, “Efficient Continuous Manifold Learning for Time Series Modeling,” arXiv preprint arXiv:2112.03379, 2021. [code]/[paper]
E. Jun, S. Jeong, D.-W. Heo, and H.-I. Suk, “Medical Transformer: Universal Brain Encoder for 3D MRI Analysis,” arXiv preprint arXiv:2104.13633, 2021. [code]/[paper]
J. Sohn, E. Jeon, W. Jung, E. Kang, and H.-I. Suk, “Fine-Grained Attention for Weakly Supervised Object Localization,” arXiv preprint arXiv:2104.04952, 2021. [code]/[paper]
C. Park and H.-I. Suk, “Deep Joint Learning of Pathological Region Localization and Alzheimer’s Disease Diagnosis,” arXiv preprint arXiv:2108.04555, 2021. [code]/[paper]

2022

Journal

W.-S. Kim#, D.-W. Heo#, J. Shen, U. Tsogt, S Odkhuu, J. Lee, E. Kang, S.-W. Kim, H.-I. Suk*, Y.-C. Chung*, "Altered Functional Connectivity in Psychotic Disorder Not Otherwise Specified," Psychiatry Research, Accepted, 2022. (JCR-IF: 11.225, Psychiatry: 9/145) (#: Co-first, *: Co-corresponding)

Paper

A. W. Mulyadi, E. Jun, and H.-I. Suk, “Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series,” IEEE Transactions on Cybernetics, Vol. 52(9), pp. 9684-9694, September 2022. (2021-JCR-IF: 11.780, AUTOMATION & CONTROL SYSTEMS: 1/63, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 5/137, COMPUTER SCIENCE, CYBERNETICS: 1/22)

Code

Paper

arXiv preprint arXiv:2003.00662

J. Phyo, W. Ko, E. Jeon, and H.-I. Suk, "TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging," IEEE Transactions on Cybernetics, Accepted, 2022 (2021-JCR-IF: 11.780)

Code

Paper

arXiv preprint arXiv:2203.12590

J.S. Yoon, M.C. Roh, and H.-I. Suk, “A Plug-in Method for Representation Factorization in Connectionist Models,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 33(8), pp. 3792-3803, August, 2022 (2021-JCR-IF: 14.255, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 6/144, COMPUTER SCIENCE, THEORY & METHODS: 4/109, COMPUTER SCIENCE, HARDWARE & ARCHITECTURE: 1/54, ENGINEERING, ELECTRICAL & ELECTRONIC: 5/276)

Code

Paper

arXiv preprint arXiv:1905.11088

K. Oh*, J.S. Yoon*, and H.-I. Suk, "Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer’s Disease Diagnosis Model," IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted, 2022. (2021-JCR-IF: 24.314, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 2/144, ENGINEERING, ELECTRICAL & ELECTRONIC: 2/276) (*: Equally contributed)

Code

Paper

arXiv preprint arXiv:2108.09451

Y. Lee*, E. Jun*, J. Choi and H.-I. Suk, “Multi-view Integrative Attention-based Deep Representation Learning for Irregular Clinical Time-series Data,” IEEE Journal of Biomedical and Health Informatics, Accepted, 2022. (2021-JCR-IF: 7.021, COMPUTER SCIENCE, INFORMATION SYSTEMS: 23/164, COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS: 21/113, MATHEMATICAL & COMPUTATIONAL BIOLOGY: 4/57, MEDICAL INFORMATICS: 7/31) (* Equally contributed)

Code

Paper

arXiv preprint arXiv:2101.09986

W. Ko, W. Jung, E. Jeon, and H.-I. Suk, “A Deep Generative–Discriminative Learning for Multi-modal Representation in Imaging Genetics,” IEEE Transactions on Medical Imaging, Vol 41(9), pp. 2348-2359, 28 March 2022. (2020-JCR-IF: 10.048, COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS: 5/111, ENGINEERING, BIOMEDICAL: 6/89, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 4/133)

Code

Paper

H.J. Park*, J.-S. Yoon*, S.S. Lee†, H.-I. Suk†, B. Park, Y.S. Sung, S.B. Hong, H. Ryu, “Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase Magnetic Resonance Imaging”, Korean Journal of Radiology, April, 2022. (*: Equally contributed, †: co-corresponding) (2020-JCR-IF: 3.500, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 48/133)

Paper

W. Ko, E. Jeon, J.S. Yoon, and H.-I. Suk, “Semi-Supervised Generative and Discriminative Adversarial Learning for Motor Imagery-based Brain-Computer Interface,” Scientific Reports, 12, 4587, March 2022. (2020-JCR-IF: 4.380, MULTIDISCIPLINARY SCIENCES: 17/72)

Code

Paper

S.S. Lee*, R. Park, Y.S. Sung, J.S. Yoon, H.-I. Suk, H.J. Kim, and S.H. Choi, “Accuracy and efficiency of right-lobe graft weight estimation using deep learning-assisted CT volumetry for living donor liver transplantation,” Diagnostics, Vol 12(3), 590, February, 2022. (2020-JCR-IF: 3.706, MEDICINE, GENERAL & INTERNAL: 45/167)

Paper

W. Ko, E. Jeon, and H.-I. Suk, “A Novel RL-assisted Deep Learning Framework for Task-informative Signals Selection and Classification for Spontaneous BCIs,” IEEE Transactions on Industrial Informatics, Vol. 18(3), pp. 1873-1882, March 2022. (2020-JCR-IF: 10.215, AUTOMATION & CONTROL SYSTEMS: 4/63, COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS: 3/111, ENGINEERING, INDUSTRIAL: 1/49)

Code

Paper

arXiv preprint arXiv:2007.00162

Conference

A. W. Mulyadi, W. Jung, K. Oh, J.S. Yoon, and H.-I. Suk, “Clinically-guided Prototype Learning and Its Use for Explanation in Alzheimer’s Disease Identification,” 2022 NeurIPS Workshop: ‘Medical Imaging meets NeurIPS (MedNeurIPS)’, New Orleans, USA, November 28-December 3, 2022. (Oral Presentation)
K. Oh, D.-W. Heo, A. W. Mulyadi, W. Jung, E. Kang, and H.-I. Suk, “Quantifying Explainability of Counterfactual-Guided MRI Feature for Alzheimer’s Disease Prediction,” 2022 NeurIPS Workshop: ‘Medical Imaging meets NeurIPS (MedNeurIPS)’, New Orleans, USA, November 28-December 3, 2022.
S. Jeong, W. Jung, J. Sohn, and H.-I. Suk, “Deep Geometrical Learning for Alzheimer’s Disease Progression Modeling,” Proc. 22nd IEEE International Conference on Data Mining (ICDM), Orlando, USA, November 28-December 1, 2022. (Acceptance Rate=9.77%)
W. Ko and H.-I. Suk, “EEG-Oriented Self-Supervised Learning and Cluster-Aware Adaptation,” 31st ACM International Conference on Information and Knowledge Management (CIKM), USA, October 17-21, 2022.
E. Kang, D.-W. Heo, and H.-I. Suk, “Prototype Learning of Inter-network Connectivity for ASD Diagnosis and Personalized Analysis,” 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Singapore, September 18-22, 2022. (Early Accept)
J. Lee, K. Oh, D. Shen, and H.-I. Suk, “A Novel Knowledge Keeper Network for 7T-Free But 7T-Guided Brain Tissue Segmentation,” 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Singapore, September 18-22, 2022. (Early Accept)
J. Phyo, W. Ko, E. Jeon, and H.-I. Suk, “Enhancing Contextual Encoding with Stage-Confusion and Stage-Transition Estimation for EEG-Based Sleep Staging,” 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, May 22-27, 2022.
S. Jeong, W. Ko, and H.-I. Suk, “Continuous Riemannian Geometric Learning for Sleep Staging Classification,” 2022 10th International Winter Conference on Brain-Computer Interface (BCI), IEEE, High1 Resort, Korea, February 21-23, 2022.

Thesis

J. Phyo, “Context-aware Multi-task Learning for EEG-based Sleep Stage Classification,” 2022 (Master Degree).

2021

Journal

J.H. Kwon, S.S. Lee, J.S. Yoon, H.-I. Suk, Y.S. Sung, H.S. Kim, C. Lee, K.M. Kim, S.J. Lee, and S.Y. Kim, “Liver-to-Spleen Volume Ratio Automatically Measured on CT Predicts Decompensation in Patients with B Viral Compensated Cirrhosis,” Korean Journal of Radiology, Vol. 22(12), pp. 1985-1995, August, 2021 (2020-JCR-IF: 3.500, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 48/133)

Paper

E. Jeon, W. Ko, J.S. Yoon, and H.-I. Suk, “Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCI,” IEEE Transactions on Neural Networks and Learning Systems, early access, pp. 1-11, August, 2021. (2019-JCR-IF: 8.793, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 10/136, COMPUTER SCIENCE, THEORY & METHODS: 3/108, COMPUTER SCIENCE, HARDWARE & ARCHITECTURE: 3/53, ENGINEERING, ELECTRICAL & ELECTRONIC: 13/266)

Code

Paper

arXiv preprint arXiv:1910.07747

D.W. Kim, J. Ha, S. Lee, J.H. Kwon, N.Y. Kim, Y. Sung, J.-S. Yoon, H.-I. Suk, Y. Lee, and B.-K. Kang, “Population-based and Personalized Reference Intervals for Liver and Spleen Volumes in healthy individuals and those with viral hepatitis,” Radiology, Vol. 301(2), pp. 339-347, August, 2021. (2020-JCR-IF: 11.105, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 2/133)

Paper

B.-K. Min*, H. S. Kim, W. Ko, M.-H. Ahn, H.-I. Suk, D. Pantazis, and R. T. Knight, “Electrophysiological Decoding of Spatial and Color Processing in Human Prefrontal Cortex,” NeuroImage, Vol. 237, pp. 118165, August, 2021 (2019-JCR-IF: 5.902, NEUROSCIENCES: 33/272, NEUROIMAGING: 1/14, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 8/134) (* Corresponding author)

Paper

W. Ko*, E. Jeon*, S. Jeong, J. Phyo, and H.-I. Suk, “A Survey on Deep Learning-based Short/Zero Calibration Approaches for EEG-based Brain-Computer Interfaces,” Frontiers Human Neuroscience, Vol. 15, pp. 643386, March, 2021. (2019-JCR-IF: 2.673) (* Equally contributed)

Paper

W. Jung, E. Jun, and H.-I. Suk, “Deep Recurrent Model for Individualized Prediction of Alzheimer’s Disease Progression,” NeuroImage, Vol. 237, pp. 118143, August, 2021. (2019-JCR-IF: 5.902, NEUROSCIENCES: 33/272, NEUROIMAGING: 1/14, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 8/134)

Code

Paper

arXiv preprint arXiv:2005.02643

J. Lee*, W. Ko*, E. Kang, and H.-I. Suk, “A Unified Framework for Personalized Regions Selection and Functional Relation Modeling for Early MCI Identification,” NeuroImage, Vol. 236, pp. 118048, August, 2021. (2019-JCR-IF: 5.902, NEUROSCIENCES: 33/272, NEUROIMAGING: 1/14, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 8/134) (* Equally contributed)

Code

Paper

W. Ko, E. Jeon, S. Jeong, and H.-I. Suk, “Multi-Scale Neural network for EEG Representation Learning in BCI,” IEEE Computational Intelligence Magazine, Vol. 16(2), pp. 31-45, April, 2021. (2019-JCR-IF: 9.083, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 9/137)

Code

Paper

arXiv preprint arXiv:2003.02657

Conference

W. Ko, W. Jung, E. Jeon, A. W. Mulyadi, and H.-I. Suk, “ENGINE: Enhancing Neuroimaging and Genetic Information by Neural Embedding,” Proc. 21st IEEE International Conference on Data Mining (ICDM), Auckland, New Zealand, December 7-10, 2021. (Acceptance Rate=20%)
W. Ko, E. Jeon, and H.-I. Suk, “Spectro-Spatio-Temporal EEG Representation Learning for Imagined Speech Recognition,” Proc. The 6th Asian Conference on Pattern Recognition (ACPR), Jeju Island, Korea, November 9-12, 2021.
J. Lee, E. Kang, E. Jeon, and H.-I. Suk, “Meta-Modulation Network for Domain Generalization in Multi-site fMRI Classification,” Proc. of 2021 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021.
W. Jung, D.-W. Heo, E. Jeon, J. Lee, and H.-I. Suk, “Inter-Regional High-level Relation Learning of Functional Connectivity via Self-Supervision,” Proc. of 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Strasbourg, France, September 27- October 1, 2021.
A. W. Mulyadi and H.-I. Suk, “ProtoBrainMaps: Prototypical Brain Maps for Alzheimer’s Disease Progression Modeling,” International Conference on Medical Imaging with Deep Learning (MIDL), Lübeck, Germany, July 7-9, 2021.
E. Jeon, W. Ko, and H.-I. Suk, “Task-relevant Deep Representation Learning via Mutual Information Maximization for BCI,” 8th International BCI Meeting, June 7-9, 2021.
W. Ko, E. Jeon, and H.-I. Suk, “Learning Informative Representation for EEG-based BCI,” 8th International BCI Meeting, June 7-9, 2021. (Student award)
S. Jeong, E. Jeon, W. Ko, and H.-I. Suk, “Fine-grained Temporal Attention Network for EEG-based Seizure Detection,” 2021 9th International Winter Conference on Brain-Computer Interface (BCI), IEEE, High1 Resort, Korea, February 22-24, 2021.

Thesis

E. Jun, “Representation Learning and Predictive Modeling on Sequential Medical Data,” 2021 (Ph.D. Degree).
C. Park, “Deep joint learning of pathological region localization and brain disease identification,” 2021 (Master Degree).
J. Lee, “Meta-Modulation Learning for Site-Invariant Brain Disease Diagnosis with Resting-State fMRI,” 2021 (Master Degree).
Y. Lee, “Multi-view Attention-based Representation Learning for Clinical Time Series Prediction,” 2021 (Master Degree).

2020

Journal

C. Lee, S.S. Lee, W.-M. Choi, K.M. Kim, Y.S. Sung, S. Lee, S.J. Lee, J.S. Yoon, and H.-I. Suk, “An Index based on Deep Learning–Measured Spleen Volume on CT for The Assessment of High-Risk Varix in B-viral Compensated Cirrhosis,” European Radiology (2019-JCR-IF: 4.101, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 21/134)

TBA

Paper

E. Jun, A. W. Mulyadi, J. Choi, and H.-I. Suk, “Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction,” IEEE Transactions on Neural Networks and Learning Systems (2019-JCR-IF: 8.793 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 10/136, COMPUTER SCIENCE, THEORY & METHODS: 3/108, COMPUTER SCIENCE, HARDWARE & ARCHITECTURE: 3/53, ENGINEERING, ELECTRICAL & ELECTRONIC: 13/266,)

Code

Paper

arXiv preprint arXiv:2003.00655

E. Jun, K.-S. Na, W. Kang, J. Lee, H.-I. Suk*, and Byungju Ham*, “Identifying Resting-State Effective Connectivity Abnormalities in Drug-Naïve Major Depressive Disorder Diagnosis via Graph Convolutional Networks,” Human Brain Mapping (*: co-corresponding) (2019-JCR-IF: 4.421, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 19/133)

Code

Paper

Y. Ahn†, J.S. Yoon†, S. Lee*, H.-I. Suk*, J. Son, Y. Sung, Y. Lee, B.-K. Kang, and H. Kim, “Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images”, Korean Journal of Radiology Vol. 21, No.8, pp. 987-997, May 2020 (†: Equally contributed, *: co-corresponding) (2019-JCR-IF: 3.179, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 37/133)

Y. Shi, H.-I. Suk, Y. Gao, S.-W. Lee, and D. Shen, “Leveraging coupled interaction for multimodal Alzheimer’s Disease Diagnosis,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 31, pp. 186-200, January 2020 [Link] (2018-JCR-IF: 11.683, COMPUTER SCIENCE, HARDWARE & ARCHITECTURE: 1/53, ENGINEERING, ELECTRICAL & ELECTRONIC: 3/266, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 2/134, COMPUTER SCIENCE, THEORY & METHODS: 1/105)

Conference

E. Jeon*, E. Kang*, J. Lee, J.Lee, T.-E. Kam, and H.-I. Suk, “Enriched Representation Learning in Resting-State fMRI for Early MCI Diagnosis,” Proc. of 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Lima, Peru (virtual), October 4-8, 2020. (*: Equally contributed)
W. Ko, K. Oh, E. Jeon, H.-I. Suk, “VIGNet: A Deep Convolutional Neural Network for EEG-based Driver Vigilance Estimation,” 2020 8th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–3, IEEE, High1 Resort, Korea, February 26-28, 2020.

Thesis

J. Lee, “A unified framework for personalized regions selection and functional relation modeling for early MCI identification,” 2020 (Master Degree).

2019

Journal

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, Vol. 202, November 2019. (2018-JCR-IF: 5.812, NEUROSCIENCES: 36/267, NEUROIMAGING: 1/14, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 11/129)

Code

Paper

B.-C. Kim*, J.S. Yoon*, Jun-Sik 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 (*: Equally contributed) (2018-JCR-IF: 5.785, NEUROSCIENCES: 37/267, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 16/134)

Code

Paper

arXiv preprint arXiv:1807.10581

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 (2018-JCR-IF: 5.812, NEUROSCIENCES: 36/267, NEUROIMAGING: 1/14, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 11/129)

Code

Paper

Conference

J.S. Yoon, W. Ko, and H.-I. Suk, “A Plug-in Factorizer for Disentangling a Latent Representation,” Proc. of 2019 ICCV Workshop on Interpreting and Explaining Visual Artificial Intelligence Models, Seoul, South Korea, November 2, 2019.
W. Jung, A. W. Mulyadi, and H.-I. Suk, “Unified Modeling of Imputation, Forecasting, and Prediction for AD Progression,” Proc. of 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 168–176, Springer, Shenzen, China, October 13-17, 2019. (Poster Presentation) (Early Accept)
E. Jeon, W. Ko, and H.-I. Suk, “Domain Adaptation with Source Selection for Motor-Imagery Based BCI,” Proc. of 7th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–4, IEEE, High1 Resort, Korea, February 18-20, 2019.
W. Ko, E. Jeon, J. Lee, and H.-I. Suk, “Semi-supervised Deep Adversarial Learning for Brain-Computer Interface,” Proc. of 7th International Winter Conference on Brain-ComputerInterface (BCI), IEEE, pp. 1–4, High1 Resort, Korea, February 18-20, 2019.
E. Jun*, A. W. Mulyadi*, and H.-I. Suk, “Stochastic Imputation and Uncertainty-Aware Attention to EHR for Mortality Prediction,” Proc. of 2019 International Joint Conference on Neural Networks(IJCNN), pp. 1–7, IEEE, Budapest, Hungary, July 14-19, 2019. (*: Equally contributed) (Oral Presentation)
E. Jun, J. Lee, B. Ham, 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 10, 2019.

Thesis

E. Lee, “Interpretable deep-learning method for early AD/MCI diagnosis with magnetic resonance imaging,” 2019 (Master Degree).

2018

Journal

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 (2018-JCR-IF: 1.770, COMPUTER SCIENCE, SOFTWARE ENGINEERING: 45/107, COMPUTER SCIENCE, INFORMATION SYSTEMS: 94/155)

Conference

J.-S. Choi, E. Lee, and H.-I. Suk, “Regional Abnormality Representation Learning in Structural MRI for AD/MCI Diagnosis,” 2018 International Workshop on Machine Learning in Medical Imaging (MLMI), Granada, Spain, September 16-20, 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.
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.

Thesis

B.-C. Kim, “Sequential multi-scale convolutional neural network for false positive reduction of pulmonary nodule detection in thoracic CT,” 2018 (Master Degree).

2017

Journal

T.-E. Kam, H.-I. Suk, and S.-W. Lee, “Multiple Functional Networks Modeling for Autism Spectrum Disorder Diagnosis,” Human Brain Mapping, Vol., 38, pp. 5804-5821, November 2017 (2016-JCR-IF: 4.530, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 12/126)

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

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)

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 (2018-JCR-IF: 6.211, ENGINEERING, ELECTRICAL & ELECTRONIC: 22/266, COMPUTER SCIENCE, THEORY & METHODS: 5/105)

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 (2018-JCR-IF: 8.880, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 5/134, COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS: 2/106, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 2/129, ENGINEERING, BIOMEDICAL: 4/80)

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 (2018-JCR-IF: 8.880, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 5/134, COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS: 2/106, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 2/129, ENGINEERING, BIOMEDICAL: 4/80)

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, 2018-JCR-IF: 12.257, ENGINEERING, BIOMEDICAL: 2/80)

Conference

J.-S. Yoon and H.-I. Suk, “Gated Two-Stage Convolutional Neural Networkfor 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

Journal

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)

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)

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)

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, 2016 (2018-JCR-IF: 3.478, ENGINEERING, BIOMEDICAL: 19/80, REHABILITATION: 5/65)

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 (2018-JCR-IF: 7.816, COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS: 3/106, ENGINEERING, ELECTRICAL & ELECTRONIC: 11/266, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 3/129, ENGINEERING, BIOMEDICAL: 5/80, IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY: 3/28)

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)

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, March 2016 (2014-JCR-IF: 2.347, BIOMEDICAL ENGINEERING: 28/76)

Conference

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

Journal

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)

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 (2018-JCR-IF: 4.046, ENGINEERING, ELECTRICAL & ELECTRONIC: 54/266)

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)

L. Wang, C.-Y. Wee, H.-I. Suk, X. Tang, 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)

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)

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)

Conference

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

Journal

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)

X. Zhu, H.-I. Suk, and D. Shen, “A Novel Matrix-Similarity Based Loss Function 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)

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)

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)

Conference

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 DiseaseDiagnosis,” 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.