Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT-Reference-Cited by-同舟云学术

Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT

Published:2019-02-18 Issue:6 Volume:44 Page:2009-2020
ISSN:2366-004X
Container-title:Abdominal Radiology
language:en
Short-container-title:Abdom Radiol

Author:

Coy Heidi^ORCID,Hsieh Kevin,Wu Willie,Nagarajan Mahesh B.,Young Jonathan R.,Douek Michael L.,Brown Matthew S.,Scalzo Fabien,Raman Steven S.

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/s00261-019-01929-0/fulltext.html

Reference47 articles.

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4. Liu N, Huang D, Cheng X, et al. Percutaneous radiofrequency ablation for renal cell carcinoma vs. partial nephrectomy: Comparison of long-term oncologic outcomes in both clear cell and non-clear cell of the most common subtype. Urol Oncol 2017; 35(8): 530.e6. https://doi.org/10.1016/j.urolonc.2017.03.014 .

5. Ishigami K, Jones AR, Dahmoush L, Leite LV, Pakalniskis MG, Barloon TJ. Imaging spectrum of renal oncocytomas: a pictorial review with pathologic correlation. Insights into Imaging 2015;6(1):53-64

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