Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …

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

The success of training computer-vision models heavily relies on the support of large-scale,
real-world images with annotations. Yet such an annotation-ready dataset is difficult to …

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

Nuclei segmentation and classification of hematoxylin and eosin-stained histology images is
a challenging task due to a variety of issues, such as color inconsistency that results from the …

Background Over the past decade, the development of molecular high-throughput methods
(omics) increased rapidly and provided new insights for cancer research. In parallel, deep …

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

Diagnosis based on histopathology for skin cancer detection is today's gold standard and
relies on the presence or absence of biomarkers and cellular atypia. However it suffers …

The exchange of large and complex slide microscopy imaging data in biomedical research
and pathology practice is impeded by a lack of data standardization and interoperability …

The pathology is decisive for disease diagnosis but relies heavily on experienced
pathologists. In recent years, there has been growing interest in the use of artificial …