Generative Deep Learning in Digital Pathology Workflows.

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David Morrison, David Harris-Birtill, Peter D Caie

Author Information

David Morrison: School of Medicine, University of St. Andrews, St. Andrews, Scotland; School of Computer Science, University of St. Andrews, St. Andrews, Scotland; Sir James Mackenzie Institute for Early Diagnosis, School of Medicine, University of St. Andrews, St. Andrews, Scotland. Electronic address: dm236@st-andrews.ac.uk.
David Harris-Birtill: School of Computer Science, University of St. Andrews, St. Andrews, Scotland.
Peter D Caie: School of Medicine, University of St. Andrews, St. Andrews, Scotland; Sir James Mackenzie Institute for Early Diagnosis, School of Medicine, University of St. Andrews, St. Andrews, Scotland.

PMID: 33838127 DOI: 10.1016/j.ajpath.2021.02.024

Many modern histopathology laboratories are in the process of digitizing their workflows. Digitization of tissue images has made it feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern computer vision techniques, based on deep learning, promises systems that can identify pathologies in slide images with a high degree of accuracy. Generative modeling 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 artifacts, 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 future directions for generative models within histopathology.

Deep Learning

Humans

Image Processing, Computer-Assisted

Models, Theoretical

Pathology

Workflow

Journal Article Research Support, Non-U.S. Gov't Review

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