Progress in digital pathology is hindered by high-resolution images and the prohibitive cost
of exhaustive localized annotations. The commonly used paradigm to categorize pathology …

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

Recent advances in whole‐slide imaging (WSI) technology have led to the development of a
myriad of computer vision and artificial intelligence‐based diagnostic, prognostic, and …

The role of tumor infiltrating lymphocytes (TILs) as a biomarker to predict disease
progression and clinical outcomes has generated tremendous interest in translational …

Transformers have been widely used in many computer vision challenges and have shown
the capability of producing better results than convolutional neural networks (CNNs). Taking …

Whole slide image (WSI) classification often relies on deep weakly supervised multiple
instance learning (MIL) methods to handle gigapixel resolution images and slide-level …

The field of digital histopathology has seen incredible growth in recent years. Digital
pathology is becoming a relevant tool in healthcare, industrial and research sectors to …

Morphological attributes from histopathological images and molecular profiles from genomic
data are important information to drive diagnosis, prognosis, and therapy of cancers. By …

Technological advancements in health sciences have led to enormous developments in
artificial intelligence (AI) models designed for application in health sectors. This article …

The appearance of histopathology images depends on tissue type, staining and digitization
procedure. These vary from source to source and are the potential causes for domain-shift …