SeongTae

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Dr. Seong Tae Kim

  • Senior Research Scientist

  • Email: seongtae.kim [@] tum.de

Short Curriculum Vitae

2019-now 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

Awards & Honors

  • Robert F. Wagner All-Conference Best Student Paper Final Lists 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

  • Program Committee Member at International Conference on Multimedia Modeling 2020
  • Reviewer for MICCAI 2019
  • Reviewer for IEEE Transactions on Cybernetics
  • Reviewer for IEEE Transactions on Image Processing
  • 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 MS or Ph.D. students)

If you are coming with third-party funding (e.g. DAAD, BK21 Plus) and interested in our research topics, then please feel free to contact me by email.

Student Projects

Feel free to contact me by email to any of the following projects. In addition to this list of offered projects, other topics are also available upon request for the student with a strong background in deep learning.

Available
Master ThesisHow to handle noisy labels in training DNN for medical applications?
(Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)

Running
IDPMultiple sclerosis lesion segmentation from Longitudinal brain MRI
(Dr. Seong Tae Kim, Ashkan Khakzar, Prof. Dr. Nassir Navab)
Master ThesisDevelopment 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 ThesisContinual and incremental learning with less forgetting strategy
(Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)
ProjectLocalization of disease with limited supervision in chest radiographs
(Dr. Seong Tae Kim, Ashkan Khakzar, Prof. Dr. Nassir Navab)
Master ThesisLearning to learn: Which data we have to annotate first in medical applications?
(Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)
Master ThesisOut-of-distribution detection in medical applications
(Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)
ProjectUnderstanding Medical Images to Generate Reliable Medical Report
(Dr. Shadi Albarqouni, Dr. Seong Tae Kim, Prof. Dr. Nassir Navab)

Publications

2019
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 Circuit Systems and Video Technology 2019.

* J. Lee, S.T. Kim, Y.M. Ro. Probenet: Probing deep networks. IEEE International Conference on Image Processing, Taipei, Taiwan, 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.

* D.H. Kim, S.T. Kim, Y.M. Ro. Latent feature representation with 3-D Multi-view convolutional neural network for bilateral analysis in digital breast tomosynthesis. IEEE International Conference on Acoustics, speech and signal processing (ICASSP), 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 (Best 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


UsersForm
Title: Dr.
Circumference of your head (in cm):  
Firstname: Seong Tae
Middlename:  
Lastname: Kim
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Birthday: 31.05.1989
Nationality: Cosmopolitan
Languages: English
Groups: Medical Imaging, Machine Learning for Medical Applications
Expertise: Medical Imaging, Computer Vision
Position: Scientific Staff
Status: Active
Emailbefore: seongtae.kim
Emailafter: tum.de
Room: MI 03.13.056
Telephone: +49 89 289 19405
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