no code implementations • 18 Oct 2022 • Jelica Vasiljević, Friedrich Feuerhake, Cédric Wemmert, Thomas Lampert
Virtual stain transfer is a promising area of research in Computational Pathology, which has a great potential to alleviate important limitations when applying deeplearningbased solutions such as lack of annotations and sensitivity to a domain shift.
no code implementations • 21 Mar 2021 • Jelica Vasiljević, Friedrich Feuerhake, Cédric Wemmert, Thomas Lampert
It has been shown that unpaired image-to-image translation methods constrained by cycle-consistency hide the information necessary for accurate input reconstruction as imperceptible noise.
no code implementations • 22 Dec 2020 • Jelica Vasiljević, Friedrich Feuerhake, Cédric Wemmert, Thomas Lampert
The application of supervised deep learning methods in digital pathology is limited due to their sensitivity to domain shift.
no code implementations • 29 Aug 2020 • Odyssee Merveille, Thomas Lampert, Jessica Schmitz, Germain Forestier, Friedrich Feuerhake, Cédric Wemmert
Objective: This article presents an automatic image processing framework to extract quantitative high-level information describing the micro-environment of glomeruli in consecutive whole slide images (WSIs) processed with different staining modalities of patients with chronic kidney rejection after kidney transplantation.
no code implementations • 17 Oct 2018 • Thomas Lampert, Odyssée Merveille, Jessica Schmitz, Germain Forestier, Friedrich Feuerhake, Cédric Wemmert
By training the network on one commonly used staining modality and applying it to images that include corresponding but differently stained tissue structures, the presented unsupervised strategies demonstrate significant improvements over standard training strategies.