Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …

The lack of annotated publicly available medical images is a major barrier for computational
research and education innovations. At the same time, many de-identified images and much …

Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …

The recent surge in performance for image analysis of digitised pathology slides can largely
be attributed to the advances in deep learning. Deep models can be used to initially localise …

Nuclei detection is one of the most fundamental and challenging problems in
histopathological image analysis, which can localize nuclei to provide effective computer …

Background Deep learning enables accurate high-resolution mapping of cells and tissue
structures that can serve as the foundation of interpretable machine-learning models for …

The success of automated medical image analysis depends on large-scale and expert-
annotated training sets. Unsupervised domain adaptation (UDA) has been raised as a …

Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained
histology images enables the extraction of interpretable cell-based features that can be used …

Multi-class cell nuclei detection is a fundamental prerequisite in the diagnosis of
histopathology. It is critical to efficiently locate and identify cells with diverse morphology and …

In computational pathology, automatic nuclei instance segmentation plays an essential role
in whole slide image analysis. While many computerized approaches have been proposed …