Digital pathology

The practice of pathology is rapidly undergoing a transformation in which multiple tools such as digital imaging, advanced artificial intelligence algorithms, and computer-aided diagnostic techniques are being linked with molecular pathology resulting in increased diagnostic power based on the ability of modern-day pathologists to use these new tools and to interpret the data generated by them.

An update for the wider enterprise imaging community about recent developments in digital pathology and goals on the horizon.

Ki67 labeling index (LI) is the preferred method of establishing grade for gastroenteropancreatic neuroendocrine tumors (GEP-NETs). There are a number of acceptable methods for calculating the Ki67 LI in GEP-NETs, but application of quantitative image analysis is a promising method to increase accuracy and repeatability. KiNet is a single-stage deep-learning-based detection and classification pipeline for Ki67 LI with performance meeting or exceeding the current state of the art.

A comprehensive curriculum for the practicing pathologists that starts with the fundamentals of digital pathology and AI and builds up knowledge to clinical implementation.

Whole slide imaging is the process of digitizing a glass slide into a single file, high magnification virtual image object, or whole …

Advances in artificial intelligence (AI), especially in deep learning, improve pathological image analysis in basic, translational, and …

Digital displays (monitors) are an indispensable component of a pathologists' daily workflow, from writing reports, viewing whole-slide …

April 12, 2017 marked a significant day in the evolution of digital pathology in the United States, when the US Food and Drug …