Generative deep learning in digital pathology workflows
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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.
Morrison , D , Harris-Birtill , D & Caie , P D 2021 , ' Generative deep learning in digital pathology workflows ' , The American Journal of Pathology , vol. 191 , no. 10 , pp. 1717-1723 . https://doi.org/10.1016/j.ajpath.2021.02.024
The American Journal of Pathology
Copyright © 2021 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1016/j.ajpath.2021.02.024.
DescriptionFunding: Supported by the Sir James Mackenzie Institute for Early Diagnosis, University of St Andrews and Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (grant number TS/S013121/1).
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