Deep Learning for Whole Slide Image Analysis: An Overview

The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images hav...

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Veröffentlicht in:Frontiers in medicine Jg. 6; S. 264
Hauptverfasser: Dimitriou, Neofytos, Arandjelović, Ognjen, Caie, Peter D.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media SA 22.11.2019
Frontiers Media S.A
Schlagworte:
Annotations
cancer
computer vision
Datasets
Deep learning
digital pathology
Digitization
Labeling
machine learning
Medical diagnosis
Medical research
Medicine
Morphology
oncology
Pathology
personalized pathology
Scanners
digital pathology
personalized pathology
computer vision
cancer
machine learning
image analysis
oncology
ISSN:2296-858X, 2296-858X
Online-Zugang:Volltext
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Zusammenfassung:The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artifacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.
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This article was submitted to Pathology, a section of the journal Frontiers in Medicine
Reviewed by: Pier Paolo Piccaluga, University of Bologna, Italy; Thomas Menter, University Hospital of Basel, Switzerland
Edited by: Inti Zlobec, University of Bern, Switzerland
ISSN:2296-858X
2296-858X
DOI:10.3389/fmed.2019.00264