Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …

The analysis of histopathology images with artificial intelligence aims to enable clinical
decision support systems and precision medicine. The success of such applications …

Despite increasing numbers of regulatory approvals, deep learning-based computational
pathology systems often overlook the impact of demographic factors on performance …

In recent years, the diagnosis of gliomas has become increasingly complex. Analysis of
glioma histopathology images using artificial intelligence (AI) offers new opportunities to …

Abstract Representation learning of pathology whole-slide images (WSIs) has been has
primarily relied on weak supervision with Multiple Instance Learning (MIL). However the …

Histopathology image evaluation is indispensable for cancer diagnoses and subtype
classification. Standard artificial intelligence methods for histopathology image analyses …

Self-supervised learning (SSL) has been successful in building patch embeddings of small
histology images (eg 224 x 224 pixels) but scaling these models to learn slide embeddings …

Digital pathology poses unique computational challenges, as a standard gigapixel slide may
comprise tens of thousands of image tiles,–. Prior models have often resorted to …

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through
standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can …

Histopathological images are widely used for the analysis of diseased (tumor) tissues and
patient treatment selection. While the majority of microscopy image processing was …