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深度学习在乳腺超声成像中的应用:综述

The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review.

作者信息

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

机构信息

Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan.

Department of Radiology, Dokkyo Medical University, Tochigi 321-0293, Japan.

出版信息

Diagnostics (Basel). 2020 Dec 6;10(12):1055. doi: 10.3390/diagnostics10121055.

Abstract

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.

摘要

乳腺癌是女性中最常被诊断出的癌症;它对女性健康构成严重威胁。因此,早期发现和恰当治疗可改善患者预后。乳腺超声是临床实践中诊断和检测乳腺癌最常用的方法之一。近年来,深度学习技术在医学图像的数据提取和分析方面取得了重大进展。因此,在临床实践中使用深度学习进行乳腺超声成像极为重要,因为它节省时间、减轻放射科医生的疲劳,并且在某些情况下弥补经验和技能的不足。这篇综述文章讨论了用于乳腺超声的深度学习的基本技术知识和算法,以及深度学习技术在图像分类、目标检测、分割和图像合成中的应用。最后,我们讨论了深度学习技术在乳腺超声中的当前问题和未来前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7378/7762151/ba161744da06/diagnostics-10-01055-g001.jpg

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