Projects per year
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 language | English |
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Pages (from-to) | 1717-1723 |
Number of pages | 6 |
Journal | The American Journal of Pathology |
Volume | 191 |
Issue number | 10 |
Early online date | 8 Apr 2021 |
DOIs | |
Publication status | Published - 1 Oct 2021 |
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Dive into the research topics of 'Generative deep learning in digital pathology workflows'. Together they form a unique fingerprint.Projects
- 1 Finished
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ICAIRD: I-CAIRD: Industrial Centre for AI Research in Digital Diagnostics
Harris-Birtill, D. C. C. (PI) & Arandelovic, O. (CoI)
1/02/19 → 31/01/23
Project: Standard