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The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review
Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women’s health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762151/ https://www.ncbi.nlm.nih.gov/pubmed/33291266 http://dx.doi.org/10.3390/diagnostics10121055 |
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author | Fujioka, Tomoyuki Mori, Mio Kubota, Kazunori Oyama, Jun Yamaga, Emi Yashima, Yuka Katsuta, Leona Nomura, Kyoko Nara, Miyako Oda, Goshi Nakagawa, Tsuyoshi Kitazume, Yoshio Tateishi, Ukihide |
author_facet | Fujioka, Tomoyuki Mori, Mio Kubota, Kazunori Oyama, Jun Yamaga, Emi Yashima, Yuka Katsuta, Leona Nomura, Kyoko Nara, Miyako Oda, Goshi Nakagawa, Tsuyoshi Kitazume, Yoshio Tateishi, Ukihide |
author_sort | Fujioka, Tomoyuki |
collection | PubMed |
description | Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women’s health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound. |
format | Online Article Text |
id | pubmed-7762151 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77621512020-12-26 The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review Fujioka, Tomoyuki Mori, Mio Kubota, Kazunori Oyama, Jun Yamaga, Emi Yashima, Yuka Katsuta, Leona Nomura, Kyoko Nara, Miyako Oda, Goshi Nakagawa, Tsuyoshi Kitazume, Yoshio Tateishi, Ukihide Diagnostics (Basel) Review Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women’s health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound. MDPI 2020-12-06 /pmc/articles/PMC7762151/ /pubmed/33291266 http://dx.doi.org/10.3390/diagnostics10121055 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Fujioka, Tomoyuki Mori, Mio Kubota, Kazunori Oyama, Jun Yamaga, Emi Yashima, Yuka Katsuta, Leona Nomura, Kyoko Nara, Miyako Oda, Goshi Nakagawa, Tsuyoshi Kitazume, Yoshio Tateishi, Ukihide The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review |
title | The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review |
title_full | The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review |
title_fullStr | The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review |
title_full_unstemmed | The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review |
title_short | The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review |
title_sort | utility of deep learning in breast ultrasonic imaging: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762151/ https://www.ncbi.nlm.nih.gov/pubmed/33291266 http://dx.doi.org/10.3390/diagnostics10121055 |
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