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

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          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.

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          Most cited references63

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Cancer statistics, 2018

            Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data, available through 2014, were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data, available through 2015, were collected by the National Center for Health Statistics. In 2018, 1,735,350 new cancer cases and 609,640 cancer deaths are projected to occur in the United States. Over the past decade of data, the cancer incidence rate (2005-2014) was stable in women and declined by approximately 2% annually in men, while the cancer death rate (2006-2015) declined by about 1.5% annually in both men and women. The combined cancer death rate dropped continuously from 1991 to 2015 by a total of 26%, translating to approximately 2,378,600 fewer cancer deaths than would have been expected if death rates had remained at their peak. Of the 10 leading causes of death, only cancer declined from 2014 to 2015. In 2015, the cancer death rate was 14% higher in non-Hispanic blacks (NHBs) than non-Hispanic whites (NHWs) overall (death rate ratio [DRR], 1.14; 95% confidence interval [95% CI], 1.13-1.15), but the racial disparity was much larger for individuals aged <65 years (DRR, 1.31; 95% CI, 1.29-1.32) compared with those aged ≥65 years (DRR, 1.07; 95% CI, 1.06-1.09) and varied substantially by state. For example, the cancer death rate was lower in NHBs than NHWs in Massachusetts for all ages and in New York for individuals aged ≥65 years, whereas for those aged <65 years, it was 3 times higher in NHBs in the District of Columbia (DRR, 2.89; 95% CI, 2.16-3.91) and about 50% higher in Wisconsin (DRR, 1.78; 95% CI, 1.56-2.02), Kansas (DRR, 1.51; 95% CI, 1.25-1.81), Louisiana (DRR, 1.49; 95% CI, 1.38-1.60), Illinois (DRR, 1.48; 95% CI, 1.39-1.57), and California (DRR, 1.45; 95% CI, 1.38-1.54). Larger racial inequalities in young and middle-aged adults probably partly reflect less access to high-quality health care. CA Cancer J Clin 2018;68:7-30. © 2018 American Cancer Society.
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              ImageNet classification with deep convolutional neural networks

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                Author and article information

                Journal
                Diagnostics (Basel)
                Diagnostics (Basel)
                diagnostics
                Diagnostics
                MDPI
                2075-4418
                06 December 2020
                December 2020
                : 10
                : 12
                : 1055
                Affiliations
                [1 ]Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; fjokmrad@ 123456tmd.ac.jp (T.F.); kubotard@ 123456dokkyomed.ac.jp (K.K.); ooymmrad@ 123456tmd.ac.jp (J.O.); ymgdrnm@ 123456tmd.ac.jp (E.Y.); 11.ruby.89@ 123456gmail.com (Y.Y.); leonah@ 123456jcom.home.ne.jp (L.K.); nomura.kyoko@ 123456kameda.jp (K.N.); miyako641@ 123456gmail.com (M.N.); ktzmmrad@ 123456tmd.ac.jp (Y.K.); ttisdrnm@ 123456tmd.ac.jp (U.T.)
                [2 ]Department of Radiology, Dokkyo Medical University, Tochigi 321-0293, Japan
                [3 ]Department of Breast Surgery, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo 113-8677, Japan
                [4 ]Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; oda.srg2@ 123456tmd.ac.jp (G.O.); nakagawa.srg2@ 123456tmd.ac.jp (T.N.)
                Author notes
                [* ]Correspondence: m_mori_116@ 123456yahoo.co.jp ; Tel.: +81-3-5803-5311; Fax: +81-3-5803-0147
                Author information
                https://orcid.org/0000-0002-7141-8901
                https://orcid.org/0000-0002-6107-4519
                https://orcid.org/0000-0002-3240-4910
                https://orcid.org/0000-0002-5931-6507
                https://orcid.org/0000-0001-5241-895X
                https://orcid.org/0000-0003-1967-0445
                Article
                diagnostics-10-01055
                10.3390/diagnostics10121055
                7762151
                33291266
                757d7d58-b315-479b-b17a-e4af71e39841
                © 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/).

                History
                : 07 November 2020
                : 05 December 2020
                Categories
                Review

                breast,ultrasound,deep learning,machine learning,artificial intelligence,neural network

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