Computational histopathology is focused on the automatic analysis of rich phenotypic
information contained in gigabyte whole slide images, aiming at providing cancer patients …

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

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 …

Background Vision transformer-based methods are advancing the field of medical artificial
intelligence and cancer imaging, including lung cancer applications. Recently, many …

Digital histopathological images, high‐resolution images of stained tissue samples, are a
vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state …

Background Deep learning has revolutionized digital pathology, allowing automatic analysis
of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. WSIs …

Digital pathology technologies, including whole slide imaging (WSI), have significantly
improved modern clinical practices by facilitating storing, viewing, processing, and sharing …

Abstract Background and Objective: Colorectal cancer (CRC) is one of the most commonly
diagnosed cancers worldwide. The accurate survival prediction for CRC patients plays a …

Survival analysis is an integral part of medical statistics that is extensively utilized to
establish prognostic indices for mortality or disease recurrence, assess treatment efficacy …

Deep learning has revolutionized digital pathology, allowing for automatic analysis of
hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. In such …