JohannesPaetzold

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

Johannes C. Paetzold

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Johannes C. Paetzold
PhD candidate

Image-Based Biomedical Modeling Group (IBBM)
Fakultät für Informatik
Technische Universität München

Office: TranslaTUM Room No. 22.2.34

Email: firstname.lastname@tum.de
Phone: 089 4140 9101

Education

  • 2018: M.Sc. in Electrical and Computer Engineering, TUM
  • 2016: B.Sc. in Engineering Science, TUM, IIT Bombay

Research Interests

  • Deep learning in Computer Vision.
  • Generative Models.
  • Baysian deep learning: Uncertainty quantification in deep learning.
  • Geometric deep learning

Student Projects

We are inviting applications for several exciting research opportunities for Master students with our group. Please also feel free to reach out if you are interested in a Theses, research project, etc. The projects are intended for students in Informatics, Physics, Electrical Engineering, or related fields. Currently, active projects include but not limited to:

Publications

2019
J. Paetzold, S. Shit, I. Ezhov, G. Tetteh, A. Ertuerk, B. Menze
clDice-a Novel Connectivity-Preserving Loss Function for Vessel Segmentation
Medical Imaging Meets NeurIPS? 2019 (Workshop), Vancouver, Canada, December 2019. (bib)
F. Navarro, S. Shit, I. Ezhov, J. Paetzold, A. Gafita, J. Peeken, S. Combs, B. Menze
Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation
Proceedings of the 10th International Workshop on Machine Learning in Medical Imaging (MLMI 2019), Shenzhen, China, October 2019. (bib)
H. Li, J. Paetzold, A. Sekuboyina, F. Kofler, Jianguo Zhang, J. Kirschke, B. Wiestler, B. Menze
DiamondGan: Unified Multi-modal Generative Adversarial Networks for MRI Sequences Synthesis
Proceedings of the 22th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Shenzhen, China, October 2019. (bib)



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