SeongTaeKim

Dr. Seong Tae Kim

I have moved to Augmented Intelligence Laboratory / Kyung Hee University

Assistant Professor

Email: st.kim [@] khu.ac.kr

Short Curriculum Vitae

2019-2021 Postdoctoral Research Associate, CAMP - Technische Universität München (TUM), Munich, Germany

2014-2019 Ph.D. Electrical Engineering,Image and Video Systems Laboratory, KAIST(Korea Advanced Institute of Science and Technology), South Korea
under the supervision of Prof. Dr. Yong Man Ro

2015 Visiting Researcher in Department of Electrical and Computer Engineering, University of Toronto, Canada
under the supervision of Prof. Dr. Konstantinos N. Plataniotis

2012-2014 M.S. Electrical Engineering,Image and Video Systems Laboratory, KAIST(Korea Advanced Institute of Science and Technology), South Korea
under the supervision of Prof. Dr. Yong Man Ro

2008-2012 B.S. Electrical Engineering, Korea University, South Korea

Research Interests

Deep Learning for Medical Image Analysis
Spatio-temporal Learning/ Longitudinal Data Analysis
Explainable/Interpretable Deep Learning
Computer-aided Diagnosis
Surgical Workflow Analysis

Awards & Honors

Selected Project for Research Credit (6K USD) at Google Cloud, 2020
Outstanding Reviewer Award at British Machine Vision Conference (BMVC) 2020
Selected Project for Research Credit (5K USD) at Google Cloud, 2019
Robert F. Wagner All-Conference Best Student Paper Award at SPIE Medical Imaging 2018, Houston, USA
Best 10% Paper Award, at IEEE International Conference on Image Processing 2015, Quebec City, Canada
Honorable Mentioned Poster Award, at SPIE Medical Imaging 2015, Orlando, USA
Research Excellence Award at School of Electrical Engineering, KAIST (2015-2017)
24th Samsung_HumanTech Honorable mentions at Samsung Electronics, 2018
23rd Samsung_HumanTech Paper Award at Samsung Electronics, 2017
Best Paper Award, at Korea Multimedia Society (2012, 2016)
Semester High Honor, at Korea University (2008, 2011)

Professional Services

Reviewer for ICCV 2021
Reviewer for MICCAI 2021
Program Committee Member at AAAI 2021
Reviewer for ECCV 2020
Reviewer for MICCAI 2020
Reviewer for BMVC 2020
Reviewer for IROS 2020
Program Committee Member at International Conference on Multimedia Modeling 2020
Reviewer for MICCAI 2019
Reviewer for IEEE Transactions on Medical Imaging
Reviewer for IEEE Transactions on Cybernetics
Reviewer for IEEE Transactions on Image Processing
Reviewer for IEEE Transactions on Multimedia
Reviewer for IEEE Transactions on Circuits Systems and Video Technology
Reviewer for Neurocomputing
Reviewer for Computers in Biology and Medicine
Reviewer for Computational and Structural Biotechnology Journal
Reviewer for Computer Methods and Programs in Biomedicine
Reviewer for Digital Signal Processing
Session chair at IEEE International Conference on Image Processing 2015 (Computer-assisted Screening and Diagnosis session)

Open Positions (Internship of graduate students)

I am always looking for strong graduate students to collaborate with. If you are coming with third-party funding and interested in our research topics, then please feel free to contact me by email.

Available

Running

Master Thesis Surgical Workflow Analysis under Limited Annotation
(Dr. Seong Tae Kim, Tobias Czempiel, Prof. Dr. Nassir Navab)

Master Thesis Interactive Segmentation for Improving Infection Quantification in CT scans
(Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)

Master Thesis Gradient Surgery for Multitask Longitudinal CT Analysis
(Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)

Master Thesis Towards Human-Like Predictor with Rejection Option
(Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)

Master Thesis Evaluating Human Skills using Deep Neural Networks
(Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)

Master Thesis Semi-supervised Active Learning
(Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)

Publications

2020

A. Ravi, S.T. Kim, F. Pfister, F. Pfister, N. Navab
Self-supervised out-of-distribution detection in brain CT scans
The first two authors contributed equally.
Medical Imaging meets NeurIPS^? workshop (bib)

M. Tirindelli, M. Victorova, J. Esteban, S.T. Kim, D. Navarro-Alarcon, Y.P. Zheng, N. Navab
Force Ultrasound Fusion: Bringing Spine Robotic-US to the Next "Level"
IEEE Robotics and Automation Letters (presented at IROS2020) (bib)

S. Denner, A. Khakzar, M. Sajid, M. Saleh, Z. Spiclin, S.T. Kim, N. Navab
Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation
The first two authors contributed equally.
BrainLes^? at International Conference on Medical Image Computing and Computer-Assisted Intervention. (bib)

T. Czempiel, M. Paschali, M. Keicher, W. Simson, H. Feußner, S.T. Kim, N. Navab
TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Lima, Peru, 2020 (The pre-print is available currently online on arXiv) (bib)

H. Lee, H. Lee, S.T. Kim, Y.M. Ro
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack
31st British Machine Vision Virtual Conference (BMVC) (bib)

L.C.O. Tiong, S.T. Kim, Y.M. Ro
Multimodal facial biometrics recognition: Dual-stream convolutional neural networks with multi-feature fusion layers
Image and Vision Computing (bib)

H. Lee, S.T. Kim, H. Lee, N. Navab, Y.M. Ro
Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation
The first two authors contributed equally.
arXivPreprint:2005.10754 (bib)

J.U. Kim, S.T. Kim, E.S. Kim, S.K. Moon, Y.M. Ro
Towards High-performance Objective Detection: Task-specific Design Considering Classification and Localization Separation
45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2020. (bib)

S.T. Kim, F. Mustaq, N. Navab
Confident Coreset for Active Learning in Medical Image Analysis
The first two authors contributed equally.
arXiv:2004.02200. (bib)

2019

A. Khakzar, S. Baselizadeh, S. Khanduja, S.T. Kim, N. Navab
Explaining Neural Networks via Perturbing Important Learned Features
pre-print version is available online at arXiv (bib)

H. Lee, S.T. Kim, J.Lee, Y.M. Ro
Realistic Breast Mass Generation through BIRADS Category
22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Shenzhen, China, 2019 (bib)

H. Lee, S.T. Kim, Y.M. Ro
Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis
MICCAI Workshop on Interpretability of Machine Intelligence in Medical Image Computing, Shenzhen, China, 2019 (bib)

H. Lee, S.T. Kim, Y.M. Ro
Building a Breast-Sentence Dataset: Its Usefulness for Computer-Aided Diagnosis
International Conference on Computer Vision (ICCV) Workshops, Seoul, Korea, 2019 (bib)

Selected publications

You can find full publication records (2014~2019) here.

* S.T. Kim, Y.M. Ro. Attended relation feature representation of facial dynamics for facial authentication. IEEE Transactions on Information Forensics and Security 2019.

* H.J. Lee, S.T. Kim, H. Lee, Y.M. Ro. Lightweight and effective facial landmark detection using adversarial learning with face geometric map generative network. IEEE Transactions on Circuits and Systems for Video Technology 2019.

* S.T. Kim, Y.M. Ro. Facial dynamics interpreter network: What are the important relations between local dynamics for facial trait estimation?. European Conference on Computer Vision(ECCV), Munich, Germany, 2018.

* J. Lee, S.T. Kim, H. Lee, Y.M. Ro. Feature2Mass: Visual feature processing in latent space for realistic labeled mass generation. European Conference on Computer Vision Workshop, Munich, Germany, 2018.

* S.T. Kim, H.M. Lee, J.H. Lee, Y.M. Ro. Visually interpretable deep network for diagnosis of breast masses on mammograms. Physics in Medicine and Biology, 2018.

* S.T. Kim, H. Lee, H.G. Kim, Y.M. Ro. ICADx: Interpretable computer-aided diagnosis of breast masses. SPIE Medical Imaging, USA (Robert F. Wagner All Conference Best Student Paper Final Lists Award), 2018.

* D.H. Kim, S.T. Kim, J.M. Chang, Y.M. Ro. Latent feature representation with depth directional long-term recurrent learning for breast masses in digital breast tomosynthesis. Physics in Medicine and Biology, 2017.

* S.T. Kim, D.H. Kim, Y.M. Ro. Detection of masses in digital breast tomosynthesis using complementary information of simulated projection. Medical Physics 2015.

* D.H. Kim, S.T. Kim, Y.M. Ro. Improving mass detection using combined feature representations from projection views and reconstructed volume of DBT and boosting based classification with feature selection. Physics in Medicine and Biology, 2015.

* D.H. Kim, S.T. Kim, Y.M. Ro. Feature extraction from bilateral dissimilarity in digital breast tomosynthesis reconstructed volume. IEEE International Conference on Image Processing, Quebec City, Canada (Best 10% paper), 2015.

* D.H. Kim, S.T. Kim, Y.M. Ro. Feature extraction from inter-view similarity of DBT projection views. SPIE Medical Imaging, Orlando, USA (Honorable Mentioned Poster Award), 2015.

* S.T. Kim, D.H. Kim, Y.M. Ro. Breast mass detection using slice conspicuity in 3D reconstructed digital breast volumes. Physics in Medicine and Biology, 2014.

Teaching

2020WS: Computer Aided Medical Procedures I

2020WS: Practical Course: Machine Learning in Medical Imaging

2020WS: Hauptseminar/Master Seminar: Deep Learning for Medical Applications

2020SS: Computer Aided Medical Procedures II

2020SS: Practical Course: Machine Learning in Medical Imaging

2020SS: Hauptseminar/Master Seminar: Deep Learning for Medical Applications

2019WS: Computer Aided Medical Procedures I

2019SS: Computer Aided Medical Procedures II

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality

		Chair for Computer Aided Medical Procedures & Augmented Reality Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality
THIS WEBPAGE IS DEPRECATED - please visit our new website

2019-2021	Postdoctoral Research Associate, CAMP - Technische Universität München (TUM), Munich, Germany
2014-2019	Ph.D. Electrical Engineering,Image and Video Systems Laboratory, KAIST(Korea Advanced Institute of Science and Technology), South Korea under the supervision of Prof. Dr. Yong Man Ro
2015	Visiting Researcher in Department of Electrical and Computer Engineering, University of Toronto, Canada under the supervision of Prof. Dr. Konstantinos N. Plataniotis
2012-2014	M.S. Electrical Engineering,Image and Video Systems Laboratory, KAIST(Korea Advanced Institute of Science and Technology), South Korea under the supervision of Prof. Dr. Yong Man Ro
2008-2012	B.S. Electrical Engineering, Korea University, South Korea

Running
Master Thesis	Surgical Workflow Analysis under Limited Annotation (Dr. Seong Tae Kim, Tobias Czempiel, Prof. Dr. Nassir Navab)
Master Thesis	Interactive Segmentation for Improving Infection Quantification in CT scans (Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)
Master Thesis	Gradient Surgery for Multitask Longitudinal CT Analysis (Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)
Master Thesis	Towards Human-Like Predictor with Rejection Option (Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)
Master Thesis	Evaluating Human Skills using Deep Neural Networks (Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)
Master Thesis	Semi-supervised Active Learning (Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)

Finished
Master Thesis	Deep Generative Model for Longitudinal Analysis (Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)
Master Thesis	Robust training of neural networks under noisy labels (Dr. Seong Tae Kim, Dr. Shadi Albarqouni, Prof. Dr. Nassir Navab)
Project	Investigation of Interpretation Methods for Understanding Deep Neural Networks (Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)
Master Thesis	Disentangled Representation Learning of Medical Brain Images using Flow-based Models (Dr. Seong Tae Kim, Matthias Keicher, Prof. Dr. Nassir Navab)
Master Thesis	Self-supervised learning for out-of-distribution detection in medical applications (Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)
Master Thesis	Learning to learn: Which data we have to annotate first in medical applications? (Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)
IDP	Multiple sclerosis lesion segmentation from Longitudinal brain MRI (Dr. Seong Tae Kim, Ashkan Khakzar, Prof. Dr. Nassir Navab)
Master Thesis	Development of spatio-temporal segmentation model for tumor volume calculation in micro-CT (Dr. Shadi Albarqouni, Dr. Seong Tae Kim, Dr. Guillaume Landry, Prof. Dr. Nassir Navab)
Master Thesis	Continual and incremental learning with less forgetting strategy (Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)
Project	Handling Imbalanced Data Problem in Chest X-ray Multi-label Classification (Dr. Seong Tae Kim, Ashkan Khakzar, Prof. Dr. Nassir Navab)
Project	Understanding Medical Images to Generate Reliable Medical Report (Dr. Shadi Albarqouni, Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)

2020
	A. Ravi, S.T. Kim, F. Pfister, F. Pfister, N. Navab Self-supervised out-of-distribution detection in brain CT scans The first two authors contributed equally. Medical Imaging meets NeurIPS^? workshop (bib)
	M. Tirindelli, M. Victorova, J. Esteban, S.T. Kim, D. Navarro-Alarcon, Y.P. Zheng, N. Navab Force Ultrasound Fusion: Bringing Spine Robotic-US to the Next "Level" IEEE Robotics and Automation Letters (presented at IROS2020) (bib)
	S. Denner, A. Khakzar, M. Sajid, M. Saleh, Z. Spiclin, S.T. Kim, N. Navab Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation The first two authors contributed equally. BrainLes^? at International Conference on Medical Image Computing and Computer-Assisted Intervention. (bib)
	T. Czempiel, M. Paschali, M. Keicher, W. Simson, H. Feußner, S.T. Kim, N. Navab TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Lima, Peru, 2020 (The pre-print is available currently online on arXiv) (bib)
	H. Lee, H. Lee, S.T. Kim, Y.M. Ro Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack 31st British Machine Vision Virtual Conference (BMVC) (bib)
	L.C.O. Tiong, S.T. Kim, Y.M. Ro Multimodal facial biometrics recognition: Dual-stream convolutional neural networks with multi-feature fusion layers Image and Vision Computing (bib)
	H. Lee, S.T. Kim, H. Lee, N. Navab, Y.M. Ro Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation The first two authors contributed equally. arXivPreprint:2005.10754 (bib)
	J.U. Kim, S.T. Kim, E.S. Kim, S.K. Moon, Y.M. Ro Towards High-performance Objective Detection: Task-specific Design Considering Classification and Localization Separation 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2020. (bib)
	S.T. Kim, F. Mustaq, N. Navab Confident Coreset for Active Learning in Medical Image Analysis The first two authors contributed equally. arXiv:2004.02200. (bib)
2019
	A. Khakzar, S. Baselizadeh, S. Khanduja, S.T. Kim, N. Navab Explaining Neural Networks via Perturbing Important Learned Features pre-print version is available online at arXiv (bib)
	H. Lee, S.T. Kim, J.Lee, Y.M. Ro Realistic Breast Mass Generation through BIRADS Category 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Shenzhen, China, 2019 (bib)
	H. Lee, S.T. Kim, Y.M. Ro Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis MICCAI Workshop on Interpretability of Machine Intelligence in Medical Image Computing, Shenzhen, China, 2019 (bib)
	H. Lee, S.T. Kim, Y.M. Ro Building a Breast-Sentence Dataset: Its Usefulness for Computer-Aided Diagnosis International Conference on Computer Vision (ICCV) Workshops, Seoul, Korea, 2019 (bib)

THIS WEBPAGE IS DEPRECATED - please visit our new website