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 m.welling@uva.nl

Co-Applicants

Radboud University Medical Center
Prof.dr. Bram van Ginneken Radboud University Medical Center bram.vanginneken@radboudumc.nl
University Medical Center Utrecht
Dr. Ivana Išgum University Medical Center Utrecht i.isgum@umcutrecht.nl

Researchers

University of Amsterdam
Shi Hu University of Amsterdam s.hu@uva.nl
University Medical Center Utrecht
Dr. Jelmer Wolterink University Medical Center Utrecht j.m.wolterink@umcutrecht.nl
Radboud University Medical Center
Ecem Lago Radboud University Medical Center ecem.lago@radboudumc.nl

Publications

2018

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