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Heung-Il Suk

Heung-Il Suk

D-Index & Metrics

Computer Science

D-Index
42
Citations
13143
World Ranking
8173
National Ranking
81

Overview

Heung-Il Suk is affiliated with Korea University in South Korea and has a research profile encompassing neuroscience, computer science, and medicine. Their work spans multiple subfields such as cognitive neuroscience, artificial intelligence, radiology, nuclear medicine and imaging, computer vision and pattern recognition, and neurology. The scientist's main research topics include machine learning applications in healthcare, functional brain connectivity studies, EEG and brain-computer interfaces, neural dynamics and brain function, brain tumor detection and classification, radiomics and machine learning in medical imaging, and the use of AI in cancer detection.

The scientist has published frequently in a variety of venues including:

  • arXiv (Cornell University)
  • UNC Libraries
  • IEEE Transactions on Neural Networks and Learning Systems
  • NeuroImage
  • Korean Journal of Radiology

Frequent co-authors collaborating with Heung-Il Suk are:

  • Eunjin Jeon
  • Jee Seok Yoon
  • Wonjun Ko
  • Wonsik Jung
  • Eunsong Kang

Selected recent papers authored or co-authored by Heung-Il Suk include:

  • Multi-Scale Neural Network for EEG Representation Learning in BCI, 2021, IEEE Computational Intelligence Magazine
  • Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images, 2020, Korean Journal of Radiology
  • TransSleep: Transitioning-Aware Attention-Based Deep Neural Network for Sleep Staging, 2022, IEEE Transactions on Cybernetics
  • A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain-Computer Interfaces, 2021, Frontiers in Human Neuroscience
  • Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks, 2020, Human Brain Mapping

Best Publications

  • Deep Learning in Medical Image Analysis

    Dinggang Shen;Guorong Wu;Heung Il Suk

  • Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.

    Heung-Il Suk;Seong-Whan Lee;Dinggang Shen

  • Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

    Heung Il Suk;Seong Whan Lee;Dinggang Shen;Dinggang Shen

  • Deep learning based imaging data completion for improved brain disease diagnosis.

    Rongjian Li;Wenlu Zhang;Heung Il Suk;Li Wang

  • Deep Learning-Based Feature Representation for AD/MCI Classification

    Heung Il Suk;Dinggang Shen

  • State-space model with deep learning for functional dynamics estimation in resting-state fMRI

    Heung Il Suk;Chong Yaw Wee;Seong Whan Lee;Dinggang Shen

  • Deep ensemble learning of sparse regression models for brain disease diagnosis

    Heung Il Suk;Seong Whan Lee;Dinggang Shen;Dinggang Shen

  • A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces

    Heung-Il Suk;Seong-Whan Lee

  • A novel relational regularization feature selection method for joint regression and classification in AD diagnosis

    Xiaofeng Zhu;Heung-Il Suk;Li Wang;Seong-Whan Lee

  • Hand gesture recognition based on dynamic Bayesian network framework

    Heung-Il Suk;Bong-Kee Sin;Seong-Whan Lee

  • A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis

    Xiaofeng Zhu;Heung Il Suk;Dinggang Shen;Dinggang Shen

  • Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification

    Xiaofeng Zhu;Heung-Il Suk;Seong-Whan Lee;Dinggang Shen

  • Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis.

    Heung Il Suk;Seong Whan Lee;Dinggang Shen;Dinggang Shen

  • Commanding a Brain-Controlled Wheelchair Using Steady-State Somatosensory Evoked Potentials

    Keun-Tae Kim;Heung-Il Suk;Seong-Whan Lee

  • Person authentication from neural activity of face-specific visual self-representation

    Seul-Ki Yeom;Heung-Il Suk;Seong-Whan Lee

  • Non-homogeneous spatial filter optimization for ElectroEncephaloGram (EEG)-based motor imagery classification

    Tae-Eui Kam;Heung-Il Suk;Seong-Whan Lee

  • Multimodal manifold-regularized transfer learning for MCI conversion prediction

    Bo Cheng;Bo Cheng;Bo Cheng;Mingxia Liu;Heung Il Suk;Dinggang Shen;Dinggang Shen

  • Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis

    Xiaofeng Zhu;Heung Il Suk;Seong Whan Lee;Dinggang Shen;Dinggang Shen

  • Subject and class specific frequency bands selection for multiclass motor imagery classification

    Heung-Il Suk;Seong-Whan Lee

  • Multi-Scale Neural Network for EEG Representation Learning in BCI

    Wonjun Ko;Eunjin Jeon;Seungwoo Jeong;Heung-Il Suk

  • Predicting BCI Subject Performance Using Probabilistic Spatio-Temporal Filters

    Heung Il Suk;Siamac Fazli;Jan Mehnert;Klaus Robert Müller

Frequent Co-Authors

Dinggang Shen
Dinggang Shen ShanghaiTech University
Seong-Whan Lee
Seong-Whan Lee Korea University
Chong Yaw Wee
Chong Yaw Wee University of North Carolina at Chapel Hill
Guorong Wu
Guorong Wu University of North Carolina at Chapel Hill
Yang Gao
Yang Gao Google (United Kingdom)
Heng Huang
Heng Huang University of Pittsburgh
Daoqiang Zhang
Daoqiang Zhang Nanjing University of Aeronautics and Astronautics
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Feng Liu
Feng Liu Tianjin Medical University General Hospital
Robert T. Knight
Robert T. Knight University of California, Berkeley

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