There is a lot of recent interest in the field of computational pathology, as many algorithms
are introduced to detect, for example, cancer lesions or molecular features. However, there …

Abstract Machine learning and artificial intelligence are poised to transform pathology.
Technologic advances have continued to develop various pathology subdomains such as …

Despite great advances, molecular cancer pathology is often limited to the use of a small
number of biomarkers rather than the whole transcriptome, partly due to computational …

Hematoxylin-and eosin (H&E) stained whole-slide images (WSIs) are the foundation of
diagnosis of cancer. In recent years, development of deep learning-based methods in …

The histopathological image has served as the gold standard of cancer diagnosis and
played a vital role in diagnosing cancer in clinical settings for over a century. Traditionally …

The modern pathology laboratory serves to provide timely and reliable pathologic
examination of tissue and liquid-based specimens from a variety of patient types and …

Anatomical Pathology dates back to the nineteenth century when Rudolf Virchow introduced
his concept of cellular pathology and when the technical improvements of light microscopy …

Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic
diagnosis of cancer is increasing as personalized cancer therapy requires accurate …

Histopathology image analysis is widely accepted as a gold standard for cancer diagnosis.
The Cancer Genome Atlas (TCGA) contains large repositories of histopathology whole slide …

One of the main obstacles for the implementation of deep convolutional neural networks
(DCNNs) in the clinical pathology workflow is their low capability to overcome variability in …