Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

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.

Journal reference: BioMedInformatics 2022, 2, 10
DOI: 10.3390/biomedinformatics2010010

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

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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