Machine learning for medical ultrasound: status, methods, and future opportunities-Reference-Cited by-同舟云学术

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Machine learning for medical ultrasound: status, methods, and future opportunities

Published:2018-02-28 Issue:4 Volume:43 Page:786-799
ISSN:2366-004X
Container-title:Abdominal Radiology
language:en
Short-container-title:Abdom Radiol

Author:

Brattain Laura J.,Telfer Brian A.,Dhyani Manish,Grajo Joseph R.,Samir Anthony E.

Funder

National Institute of Biomedical Imaging and Bioengineering

Publisher

Springer Science and Business Media LLC

Subject

Urology,Gastroenterology,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

http://link.springer.com/article/10.1007/s00261-018-1517-0/fulltext.html

Reference135 articles.

1. Wang S, Summers RM (2012) Machine learning and radiology. Med Image Anal 16(5):933–951

2. Shen D, Wu G, Suk H-I (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248

3. Litjens G et al. (2017) A survey on deep learning in medical image analysis. ArXiv Prepr. ArXiv170205747

4. Ravi D, et al. (2017) Deep Learning for health informatics. IEEE J Biomed Health Inform 21(1):4–21

5. Cassinotto C, et al. (2014) Non-invasive assessment of liver fibrosis with impulse elastography: comparison of Supersonic Shear Imaging with ARFI and FibroScan®. J. Hepatol. 61(3):550–557

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