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Deep Learning and Its Applications in Computational Pathology
Version 1
: Received: 13 January 2022 / Approved: 17 January 2022 / Online: 17 January 2022 (12:31:25 CET)
A peer-reviewed article of this Preprint also exists.
Hong, R.; Fenyö, D. Deep Learning and Its Applications in Computational Pathology. BioMedInformatics 2022, 2, 159-168. Hong, R.; Fenyö, D. Deep Learning and Its Applications in Computational Pathology. BioMedInformatics 2022, 2, 159-168.
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.
Keywords
deep learning; machine learning; histopathology; computational pathology; convolutional neural networks; generative adversarial networks
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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