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- W4205124067 abstract "Deep learning techniques, such as convolutional neural networks (CNN), generative adversarial networks (GAN), and graph neural networks (GNN), have over the past decade changed the ac-curacy of prediction in many diverse fields. In recent years, the application of deep learning tech-niques in computer vision tasks in pathology demonstrated extraordinary potential in assisting clinicians, automating diagnosis, and reducing costs for patients. Formerly unknown pathologi-cal evidence, such as morphological features related to specific biomarkers, copy number varia-tions, and other molecular features, were also able to be captured by deep learning models. In this paper, we review popular deep learning methods and some recent publications about their appli-cations in pathology." @default.
- W4205124067 created "2022-01-25" @default.
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- W4205124067 date "2022-01-17" @default.
- W4205124067 modified "2023-10-16" @default.
- W4205124067 title "Deep Learning and Its Applications in Computational Pathology" @default.
- W4205124067 doi "https://doi.org/10.20944/preprints202201.0224.v1" @default.
- W4205124067 hasPublicationYear "2022" @default.
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