Search Results for author:
Found 5 papers, 0 papers with code
HistoStarGAN: A Unified Approach to Stain Normalisation, Stain Transfer and Stain Invariant Segmentation in Renal Histopathology
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.
Self adversarial attack as an augmentation method for immunohistochemical stainings
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.
Towards Histopathological Stain Invariance by Unsupervised Domain Augmentation using Generative Adversarial Networks
The application of supervised deep learning methods in digital pathology is limited due to their sensitivity to domain shift.
An automatic framework for fusing information from differently stained consecutive digital whole slide images: A case study in renal histology
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.
Strategies for Training Stain Invariant CNNs
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.
