Towards computationally efficient prediction of molecular signatures from routine histology images
Year: 2021
Type: article
Abstract: Identification of actionable genomic alterations in diagnostic tissue samples provides key information for personalised cancer treatment. However, current diagnostic tests used to predict the status of molecular pathways from standard histopathology material are commonly tissue destructive, generate relevant costs, and can take hours or even days to return conclusive results. Relating molecular alterations to digital image information derived from standard haematoxylin and eosin-stained histopathology slides has become a task that machine learning models are able to solve across multiple cancer types (appendix).
Source: The Lancet Digital Health
Authors: Maxime W Lafarge, Viktor H Koelzer
Institutions: University of Zurich, University Hospital Zurich
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $5,000
Funder: Medical Research Council
OpenAlex ID: W3206035367