arXiv

2024

Conference

2023

Journal

ALearnableCounterCondition"

W.-S. Kim#, D.-W. Heo#, J. Maeng, J. Shen, U. Tsogt, S. Odkhuu, X. Zhang, S. Cheraghi, S.-W. Kim, B.-J. Ham, F.Z. Rami, J. Sui, C.Y. Kang, H.-I. Suk*, and Y.-C. Chung*, "Deep Learning-based Brain Age Prediction in Patients with Schizophrenia Spectrum Disorders," Schizophrenia Bulletin, December, 2023. (2023-JCR-IF: 6.6, PSYCHIATRY: 32/155) (*: Co-first, *: Co-corresponding)

ALearnableCounterCondition"

E. Kang, D. Heo, J. Lee, and H.-I. Suk, "A Learnable Counter-condition Analysis Framework for Functional Connectivity-based Neurological Disorder Diagnosis," IEEE Transactions on Medical Imaging, Early Access, 2023. (2023-JCR-IF: 10.6, COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS: 6/110)

EAG-RS"

W. Jung, E. Jeon, E. Kang, and H.-I. Suk, "EAG-RS: A Novel Explainability-guided ROI-Selection Framework for ASD Diagnosis via Inter-regional Relation Learning," IEEE Transactions on Medical Imaging, Early Access, 2023. (2023-JCR-IF: 10.6, COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS: 6/110)

MoANA"

J. Sohn, E. Jeon, W. Jung, E. Kang, and H.-I. Suk, “Module of Axis-based Nexus Attention for Weakly Supervised Object Localization,” Scientific Reports, Vol. 13, No. 18588, October, 2023. (2022-JCR-IF: 4.6, MULTIDISCIPLINARY SCIENCES 22/73)

Deep

S. Jeong, W. Ko, A. W. Mulyadi, and H.-I. Suk, “Deep Efficient Continuous Manifold Learning for Time Series Modeling,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 46, No. 1, pp. 171-184, January, 2024. (Early Access: September, 2023)(2022-JCR-IF: 23.6, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 2/145, ENGINEERING, ELECTRICAL & ELECTRONIC: 2/275)

Spatiotemporal Graph Neural Networks for Predicting Mid-to-Long-Term PM2.5 Concentrations

D.-Y. Kim, D.-Y. Jin, and H.-I. Suk, “Spatiotemporal graph neural networks for predicting mid-to-long-term PM2.5 concentrations,” Journal of Cleaner Production, Vol. 425, p. 138880, September, 2023. (Early Access: September, 2023)(2022-JCR-IF: 11.1, ENVIRONMENTAL SCIENCES: 22/274)

Site-Invariant Meta-Modulation Learning for Multi-Site Autism Spectrum Disorders Diagnosis

J. Lee, E. Kang, D.-W. Heo, and H.-I. Suk, “Site-invariant meta-modulation learning for multisite autism spectrum disorders diagnosis,” IEEE Transactions on Neural Networks and Learning Systems, early access, September, 2023. (2022-JCR-IF: 10.4, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 14/145, COMPUTER SCIENCE, HARDWARE & ARCHITECTURE: 3/54)

Medical Transformer: Universal Brain Encoder for 3D MRI Analysis

E. Jun, S. Jeong, D.-W. Heo, and H.-I. Suk, “Medical transformer: Universal encoder for 3-D brain MRI analysis,” IEEE Transactions on Neural Networks and Learning Systems, early access, September, 2023. (2022-JCR-IF: 10.4, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 14/145, COMPUTER SCIENCE, HARDWARE & ARCHITECTURE: 3/54)

Deep Joint Learning of Pathological Region Localization and Alzheimer's Disease Diagnosis

C. Park*, W. Jung*, and H.-I. Suk, “Deep joint learning of pathological region localization and alzheimer’s disease diagnosis,” Scientific Reports, Vol. 13, No. 11664, 2023. (2021-JCR-IF: 4.997, MULTIDISCIPLINARY SCIENCES: 22/73)(*: Equally contributed)

Estimating Explainable Alzheimer’s Disease Likelihood Map via Clinically-guided Prototype Learning

A. W. Mulyadi, W. Jung, K. Oh, J. S. Yoon, K. H. Lee, and H.-I. Suk, “Estimating Explainable Alzheimer’s Disease Likelihood Map via Clinically-guided Prototype Learning,” NeuroImage, Vol. 273, p. 120073, June, 2023. (2021-JCR-IF: 7.400, NEUROSCIENCES: 38/275, NEUROIMAGING: 2/14, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 15/136)

J.Y. Choi, S.S. Lee, N.Y. Kim, H.J. Park, Y.S. Sung, Y. Lee, J.S. Yoon, and H.-I. Suk, "The Effect of Hepatic Steatosis on Liver Volume Determined by Proton Density Fat Fraction and Deep Learning–Measured Liver Volume," European Radiology, pp. 1-9, April, 2023 (2021-JCR-IF: 7.034).

W.-S. Kim, D.-W. Heo, J. Shen, U. Tsogt, S. Odkhuu, S.-W. Kim, H.-I. Suk, B.-J. Ham, F.Z. Rami, C.Y. Kang, J. Sui, and Y.-C. Chung, "Stage-Specific Brain Aging in First-Episode Schizophrenia and Treatment-Resistant Schizophrenia," International Journal of Neuropsychopharmacology, Vol. 26, No. 3, pp. 207-216, March 2023 (2021-JCR-IF: 5.310).

Conference

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, Vol. 317, p. 114871, November, 2022. (JCR-IF: 11.225, Psychiatry: 9/145) (#: Co-first, *: Co-corresponding)

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, Vol. 53, No. 7, pp. 4500–4510, July, 2023. (Early Access: September, 2022) (2021-JCR-IF: 11.780)

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, Vol. 45, No. 4, pp. 4843-4857, April, 2023. (Early Access: August, 2022)(2021-JCR-IF: 24.314, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 2/144, ENGINEERING, ELECTRICAL & ELECTRONIC: 2/276) (*: Equally contributed)

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, Vol. 26, No. 8, pp. 4270–4280, August, 2022. (Early Access: May, 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)

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, No. 9, pp. 2348-2359, September, 2022. (Early Access: March, 2022)(2020-JCR-IF: 10.048, COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS: 5/111, ENGINEERING, BIOMEDICAL: 6/89, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 4/133)

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 MRI,” Korean Journal of Radiology, Vol. 23, No. 7, p. 720, April, 2022. (*: Equally contributed, †: co-corresponding) (2020-JCR-IF: 3.500, RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING: 48/133)

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, Vol. 12, p. 4587, March, 2022. (2020-JCR-IF: 4.380, MULTIDISCIPLINARY SCIENCES: 17/72)

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, No. 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)

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, No. 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)

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, No. 3, p. 590, February, 2022. (2020-JCR-IF: 3.706, MEDICINE, GENERAL & INTERNAL: 45/167)

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, No. 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)

Conference

Thesis

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)

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, vol. 34, no. 2, pp. 739-749, February, 2023. (Early Access: 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)

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)

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)

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)

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)

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)

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, no. 2, pp. 31-45, May, 2021. (Early Access: April, 2021)(2019-JCR-IF: 9.083, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 9/137)

Conference

Thesis

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)

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,)

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)

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

Thesis

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)

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)

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)

Conference

Thesis

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

Thesis

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

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

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

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

Welcome to The Machine Intelligence Lab

The Machine Intelligence Lab (MILAB) at Korea University is directed by Professor Heung-Il Suk.

We are tackling fundamental open problems in developing biologically plausible machine/deep learning algorithms for various applications including medical/computer vision, brain-computer interfaces, healthcare, and neuroinformatics.

Research Area

Research Area

  • Area 1

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Title 1

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam ut tortor ac dui blandit tempus. Praesent ultricies velit a congue scelerisque. Phasellus vitae libero at elit consectetur molestie. Duis sit amet metus tortor. Cras dolor magna, ultrices eu fermentum eu, accumsan ut quam. Nunc ante massa, vulputate in eros nec, vestibulum porta ligula.

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Title 2

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam ut tortor ac dui blandit tempus. Praesent ultricies velit a congue scelerisque. Phasellus vitae libero at elit consectetur molestie. Duis sit amet metus tortor. Cras dolor magna, ultrices eu fermentum eu, accumsan ut quam. Nunc ante massa, vulputate in eros nec, vestibulum porta ligula.

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Title 3

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam ut tortor ac dui blandit tempus. Praesent ultricies velit a congue scelerisque. Phasellus vitae libero at elit consectetur molestie. Duis sit amet metus tortor. Cras dolor magna, ultrices eu fermentum eu, accumsan ut quam. Nunc ante massa, vulputate in eros nec, vestibulum porta ligula.

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Our lab’s latest news

Accepted to ICASSP2024

Accepted to ICASSP2024

Congratulations!!! Jiwon’s work on “Eigendecomposition-Based Spatial-Temporal Attention for Brain Cognitive States Identification,” has been accepted for publication in ICASSP2024.

We are hiring! Prospective students may contact the professor.