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Found 3 papers, 1 papers with code
Valuing Vicinity: Memory attention framework for context-based semantic segmentation in histopathology
The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely.
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.
