1 citations · 1 across the 2 of their papers we have counts for
5 papers
Self adversarial attack as an augmentation method for immunohistochemical stainings
Jelica Vasiljević, Friedrich Feuerhake, Cédric Wemmert +1
It has been shown that unpaired image-to-image translation methods constrained by cycle-consistency hide the information necessary for accurate input reconstruction as imperceptibl…
Towards Histopathological Stain Invariance by Unsupervised Domain Augmentation using Generative Adversarial Networks
Jelica Vasiljević, Friedrich Feuerhake, Cédric Wemmert +1
The application of supervised deep learning methods in digital pathology is limited due to their sensitivity to domain shift. Digital Pathology is an area prone to high variability…
Strategies for Training Stain Invariant CNNs
Thomas Lampert, Odyssée Merveille, Jessica Schmitz +3
An important part of Digital Pathology is the analysis of multiple digitised whole slide images from differently stained tissue sections. It is common practice to mount consecutive…
Context-based Normalization of Histological Stains using Deep Convolutional Features
Daniel Bug, Steffen Schneider, Anne Grote +4
While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to ac…
Why one-size-fits-all vaso-modulatory interventions fail to control glioma invasion: in silico insights
J. C. L. Alfonso, A. Kohn-Luque, T. Stylianopoulos +3
There is an ongoing debate on the therapeutic potential of vaso-modulatory interventions against glioma invasion. Prominent vasculature-targeting therapies involve functional tumou…