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 University Medical Center Utrecht email@example.com|
|Shi Hu University of Amsterdam firstname.lastname@example.org|
|Dr. Jelmer Wolterink University Medical Center Utrecht email@example.com|
Medical Imaging with Deep Learning. MIDL Amsterdam, 2018.