Histology permits the observation of otherwise invisible structures of the internal topography
of a specimen. Although it enables the investigation of tissues at a cellular level, it is invasive …

Abstract Purpose of Review Our goal is to provide an overview of machine learning methods
and artificial intelligence in digital pathology image analysis. We also highlight novel …

A central challenge in biology is obtaining high-content, high-resolution information while
analyzing tissue samples at volumes relevant to disease progression. We address this here …

Interest in spatial omics is on the rise, but generation of highly multiplexed images remains
challenging, due to cost, expertise, methodical constraints, and access to technology. An …

Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to
compare the performance of image registration algorithms on several kinds of microscopy …

Abstract Three-dimensional (3D) optical imaging of whole biological organs with
microscopic resolution has remained a challenge. Most versions of such imaging techniques …

Explainable artificial intelligence (XAI) is essential for enabling clinical users to get informed
decision support from AI and comply with evidence-based medical practice. Applying XAI in …

Pancreatic cancer is currently the third leading cause of cancer death in the United States.
The clinical hallmarks of this disease include abdominal pain that radiates to the back, the …

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for
research and clinical applications. Advances in computing, deep learning, and availability of …

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest forms of cancer.
Accumulating evidence indicates the tumor microenvironment is highly associated with …