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5. Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT. Coy H; Hsieh K; Wu W; Nagarajan MB; Young JR; Douek ML; Brown MS; Scalzo F; Raman SS Abdom Radiol (NY); 2019 Jun; 44(6):2009-2020. PubMed ID: 30778739 [TBL] [Abstract][Full Text] [Related]
6. Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation. Wentland AL; Yamashita R; Kino A; Pandit P; Shen L; Brooke Jeffrey R; Rubin D; Kamaya A Abdom Radiol (NY); 2023 Feb; 48(2):642-648. PubMed ID: 36370180 [TBL] [Abstract][Full Text] [Related]
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11. Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat. Yang R; Wu J; Sun L; Lai S; Xu Y; Liu X; Ma Y; Zhen X Eur Radiol; 2020 Feb; 30(2):1254-1263. PubMed ID: 31468159 [TBL] [Abstract][Full Text] [Related]
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14. Automated differentiation of benign renal oncocytoma and chromophobe renal cell carcinoma on computed tomography using deep learning. Baghdadi A; Aldhaam NA; Elsayed AS; Hussein AA; Cavuoto LA; Kauffman E; Guru KA BJU Int; 2020 Apr; 125(4):553-560. PubMed ID: 31901213 [TBL] [Abstract][Full Text] [Related]
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