Jörg Sander

PhD Candidate

Department of Biomedical Engineering & Physics
E-mail: j [dot] sander1 [at] amsterdamumc [dot] nl
Phone: +31 56 60 226
LinkedIn; Google Scholar


Jörg received his Master of Arts in Experimental Psychology at the University of Leiden in 1997 and his MSc in Artificial Intelligence at the University of Amsterdam in 2017. His Master thesis entitled ”Combining adaptive-computation-time and learning-to-learn approaches for optimizing loss functions of base-learners”.

In 2018, Jörg joined the Image Science Institute as a Ph.D. candidate under the supervision of Prof. dr. Ivana Išgum. In 2019, he moved with the group to the Amsterdam AMC. His research, which is part of the program Deep learning in medical image analysis (DLMedIA), focuses on the development of learning strategies that allow learning systems (aka Deep Neural Networks) to continuously learn on different tasks w.r.t. cardiac MRI image analysis.


Journal Articles

1.

J. Sander, B.D. de Vos, I. Išgum

Automatic segmentation with detection of local segmentation failures in cardiac MRI Journal Article

Scientific Reports, 10 (21769 ), 2020.

Abstract | Links | BibTeX

Inproceedings

1.

J. Sander, B.D. de Vos, I. Išgum

Unsupervised super-resolution: creating high-resolution medical images from low-resolution anisotropic examples Inproceedings

In: SPIE Medical Imaging, pp. 115960E, 2021.

Abstract | Links | BibTeX

2.

B.D. de Vos, B.H.M. van der Velden, J. Sander, K.G.A. Gilhuijs, M. Staring, I. Išgum

Mutual information for unsupervised deep learning image registration Inproceedings

In: SPIE Medical Imaging, pp. 113130R, 2020.

Abstract | Links | BibTeX

3.

J. Sander, B.D. de Vos, J.M. Wolterink, I. Išgum

Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI Inproceedings

In: SPIE Medical Imaging, pp. 1094919, 2019.

Abstract | Links | BibTeX