116 citations · 117 across the 3 of their papers we have counts for
5 papers
'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient
Christian Strack, Kelsey L. Pomykala, Heinz-Peter Schlemmer +2
With the rise in importance of personalized medicine, we trained personalized neural networks to detect tumor progression in longitudinal datasets. The model was evaluated on two d…
Automated brain extraction of multi-sequence MRI using artificial neural networks
Fabian Isensee, Marianne Schell, Irada Tursunova +10
Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses. The majority of brain extraction a…
Domain Adaptation for Deviating Acquisition Protocols in CNN-based Lesion Classification on Diffusion-Weighted MR Images
Jennifer Kamphenkel, Paul F. Jaeger, Sebastian Bickelhaupt +8
End-to-end deep learning improves breast cancer classification on diffusion-weighted MR images (DWI) using a convolutional neural network (CNN) architecture. A limitation of CNN as…
Adversarial Networks for Prostate Cancer Detection
Simon Kohl, David Bonekamp, Heinz-Peter Schlemmer +5
The large number of trainable parameters of deep neural networks renders them inherently data hungry. This characteristic heavily challenges the medical imaging community and to ma…
Adversarial Networks for the Detection of Aggressive Prostate Cancer
Simon Kohl, David Bonekamp, Heinz-Peter Schlemmer +5
Semantic segmentation constitutes an integral part of medical image analyses for which breakthroughs in the field of deep learning were of high relevance. The large number of train…