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- Email: st.kim [@] khu.ac.kr
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Short Curriculum Vitae
| 2019-2021 | Postdoctoral Research Associate, CAMP - Technische Universität München (TUM), Munich, Germany
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| 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
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| 2015 | Visiting Researcher in Department of Electrical and Computer Engineering, University of Toronto, Canada
under the supervision of Prof. Dr. Konstantinos N. Plataniotis
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| 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
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| 2008-2012 | B.S. Electrical Engineering, Korea University, South Korea
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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.
| 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) |
Publications
| 2020 |
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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| 2019 |
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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)
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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)
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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)
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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)
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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