Generative deep learning in digital pathology workflows

David Morrison*, David Harris-Birtill, Peter D. Caie

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

7 Downloads (Pure)

Abstract

Many modern histopathology laboratories are in the process of digitising their workflows. Once images of the tissue exist as digital data, it becomes feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern computer vision techniques, based on Deep Learning, promise systems that can identify pathologies in slide images with a high degree of accuracy. Generative modelling is an approach to machine learning and deep learning that can be used to transform and generate data. It can be applied to a broad range of tasks within digital pathology including the removal of color and intensity artefacts, the adaption of images in one domain into those of another, and the generation of synthetic digital tissue samples. This review provides an introduction to the topic, considers these applications, and discusses some future directions for generative models within histopathology.
Original languageEnglish
Pages (from-to)1717-1723
Number of pages6
JournalThe American Journal of Pathology
Volume191
Issue number10
Early online date8 Apr 2021
DOIs
Publication statusPublished - 1 Oct 2021

Fingerprint

Dive into the research topics of 'Generative deep learning in digital pathology workflows'. Together they form a unique fingerprint.

Cite this