Deep learning with a convolutional neural network model to differentiate renal parenchymal tumors: a preliminary study
Author:
Publisher
Springer Science and Business Media LLC
Subject
Urology,Gastroenterology,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology
Link
https://link.springer.com/content/pdf/10.1007/s00261-021-02981-5.pdf
Reference40 articles.
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3. Mehta V, Venkataraman G, Antic T, Rubinas TC, Le Pool IC, Picken MM. (2013) Renal angiomyolipoma, fat-poor variant – a clinicopathologic mimicker of malignancy. Virchows Arch 463:41-46.
4. Ljungberg B, Bensalah K, Canfield S, et al. (2015) EAU Guidelines on renal cell carcinoma: 2014 update. Eur Urol 67:913-924.
5. Tzortzakakis A, Gustafsson O, Karlsson M, Ekström-Ehn L, Ghaffarpour R, Axelsson R. (2017) Visual evaluation and differentiation of renal oncocytomas from renal cell carcinomas by means of 99mTc-sestamibi SPECT/CT. Ejnmmi Res 7:29.
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