Computer scientists working with pathologists have trained an AI tool to determine which patients with lung cancer have a higher risk of their disease coming back after treatment.
Pathologists who examined the computationally stained images could not tell them apart from traditionally stained slides.
To better leverage cancer data for research, scientists are developing an artificial intelligence-based natural language processing tool to improve information extraction from textual pathology reports.
By adding infrared capability to the ubiquitous, standard optical microscope, researchers hope to bring cancer diagnosis into the digital era.
A novel method of combining advanced optical imaging with an artificial intelligence algorithm produces accurate, real-time intraoperative diagnosis of brain tumors.
An AI has successfully found features in pathology images from human cancer patients, without annotation, that could be understood by human doctors.
Researchers used artificial intelligence to develop a new classification method which identifies the primary origins of cancerous tissue based on chemical DNA changes.
Researchers have utilized machine learning capabilities to assist with the challenging task of grading tumor patterns and subtypes of lung adenocarcinoma.