Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features-Reference-Cited by-同舟云学术

Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features

Published:2019-05-15 Issue:7 Volume:29 Page:3348-3357
ISSN:0938-7994
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Wang Clinton J.,Hamm Charlie A.,Savic Lynn J.,Ferrante Marc,Schobert Isabel,Schlachter Todd,Lin MingDe,Weinreb Jeffrey C.,Duncan James S.,Chapiro Julius,Letzen Brian

Funder

National Cancer Institute

RSNA Research and Education Foundation

Publisher

Springer Science and Business Media LLC

Subject

Radiology Nuclear Medicine and imaging,General Medicine

Link

http://link.springer.com/content/pdf/10.1007/s00330-019-06214-8.pdf

Reference31 articles.

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2. Chartrand G, Cheng PM, Vorontsov E et al (2017) Deep learning: a primer for radiologists. Radiographics 37:2113–2131

3. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35:1207–1216

4. Greenspan H, Van Ginneken B, Summers RM (2016) Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging 35:1153–1159

5. Hamm CA, Wang CJ, Savic LJ et al (2019) Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol. https://doi.org/10.1007/s00330-019-06205-9

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