Project 1.2 - Deep Generative Models

In medical applications large data sets are generally not available, or they are unbalanced (containing much fewer abnormal, e.g. cancer cases, than normal data). We address the challenge of simulating abnormal training data by generative deep learning. The new technology is based on variational autoencoders, with approximate Bayesian computation and extensions for semi-supervised learning.

Project Leader

University of Amsterdam
Prof.dr. Max Welling University of Amsterdam


Radboud University Medical Center
Prof.dr. Bram van Ginneken Radboud University Medical Center
Amsterdam UMC
Dr. Ivana Išgum Amsterdam UMC


University of Amsterdam
Shi Hu University of Amsterdam
Amsterdam UMC
Dr. Jelmer Wolterink Amsterdam UMC



J.M. Wolterink, T. Leiner, I. Išgum

Graph convolutional networks for coronary artery segmentation in cardiac CT angiography Inproceedings

1st International Workshop on Graph Learning in Medical Image (GLMI 2019), in press, 2019.



J.M. Wolterink, T. Leiner, I. Isgum

Blood vessel geometry synthesis using generative adversarial networks Inproceedings

Medical Imaging with Deep Learning. MIDL Amsterdam, 2018.

Abstract | Links | BibTeX