Authors:
Madison Rose
1
;
Joseph Geradts
2
and
Nic Herndon
1
Affiliations:
1
Department of Computer Science, East Carolina University, Greenville, North Carolina, U.S.A.
;
2
Department of Pathology, Brody School of Medicine, East Carolina Univesity, Greenville, North Carolina, U.S.A.
Keyword(s):
Breast Cancer, Machine Learning, Deep Learning, Digital Pathology, Convolutional Neural Networks, Whole Slide Imaging.
Abstract:
The development of scanners capable of whole slide imaging has transformed digital pathology. There have been many benefits to being able to digitize a stained-glass slide from a tissue sample, but perhaps the most impactful one has been the introduction of machine learning in digital pathology. This has the potential to revolutionize the field through increased diagnostic accuracy as well as reduced workload on pathologists. In the last few years, a wide range of machine learning techniques have been applied to various tasks in digital pathology, with deep learning and convolutional neural networks being arguably the most popular choice. Breast cancer, as one of the most common cancers among women worldwide, has been a topic of wide interest since hematoxylin and eosin-stained (H&E)-stained slides can be used for breast cancer diagnosis. This paper summarizes key advancements in digital breast pathology with a focus on whole slide image analysis and provides insight into popular met
hods to overcome key challenges in the industry.
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