Computational Pathology

Association for Pathology Informatics (API) Companion Meeting; members of the Journal of Pathology Informatics (JPI) editorial board provide an overview of the clinical and technical questions one must ask when reading/reviewing these articles.

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.