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.
|Prof.dr. Max Welling University of Amsterdam email@example.com|
|Prof.dr. Bram van Ginneken Radboud University Medical Center firstname.lastname@example.org|
|Dr. Ivana Išgum Amsterdam UMC email@example.com|
|Shi Hu University of Amsterdam firstname.lastname@example.org|
|Dr. Jelmer Wolterink Amsterdam UMC email@example.com|
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.
Medical Imaging with Deep Learning. MIDL Amsterdam, 2018.