Deep Learning in Digital Breast Pathology

Madison Rose, Joseph Geradts, Nic Herndon

2024

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 methods to overcome key challenges in the industry.

Paper Citation

in Harvard Style

Rose M., Geradts J. and Herndon N. (2024). Deep Learning in Digital Breast Pathology. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS; ISBN 978-989-758-688-0, SciTePress, pages 404-414. DOI: 10.5220/0012576100003657

in Bibtex Style

@conference{bioinformatics24,
author={Madison Rose and Joseph Geradts and Nic Herndon},
title={Deep Learning in Digital Breast Pathology},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2024},
pages={404-414},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012576100003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS
TI - Deep Learning in Digital Breast Pathology
SN - 978-989-758-688-0
AU - Rose M.
AU - Geradts J.
AU - Herndon N.
PY - 2024
SP - 404
EP - 414
DO - 10.5220/0012576100003657
PB - SciTePress