A regulatory science initiative to harmonize and standardize digital pathology and machine learning processes to speed up clinical innovation to patients

A regulatory science initiative to harmonize and standardize digital pathology and machine learning processes to speed up clinical innovation to patients

Unlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imagi...

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Bibliographic Details
Main Authors: Hetal Desai Marble, Richard Huang, Sarah Nixon Dudgeon, Amanda Lowe, Markus D Herrmann, Scott Blakely, Matthew O Leavitt, Mike Isaacs, Matthew G Hanna, Ashish Sharma, Jithesh Veetil, Pamela Goldberg, Joachim H Schmid, Laura Lasiter, Brandon D Gallas, Esther Abels, Jochen K Lennerz
Format: Article
Language:English
Published: Wolters Kluwer Medknow Publications 2020-01-01
Series:Journal of Pathology Informatics
Subjects:
artificial intelligence
digital pathology
machine learning
regulatory science
slide scanning
Online Access:http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=22;epage=22;aulast=Marble

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http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=22;epage=22;aulast=Marble

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