A Deep Learning Framework for Kidney Stone Prediction
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7137-4_8
Reference18 articles.
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2. Zabihollahy F, Schieda N, Krishna S, Ukwatta E (2020) Automated classification of solid renal masses on contrast enhanced computed tomography images using convolutional neural network with decision fusion. Eur Radiol 30(9):5183–5190
3. Schieda N, Nguyen K, Ornhill K, McInnes MDF, Wu M, James N (2020) Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic ct. Abdom Radiol 45(9):2786–2796
4. Yap FY, Varghese BA, Cen SY et al (2020) Shape and texturebasedradiomics signature on CT effectively discriminates benign from malignant renal masses. Eur Radiol 31(2):1011–1021
5. Aminsharifi A, Irani D, Tayebi S, JafariKafash T, Shabanian T, Parsaei H (2020) Predicting the postoperative outcome of percutaneous nephrolithotomy with machine learning system: software validation and comparative analysis with guy’s stone score and the CROES nomogram. J Endourol 34(6):692–699. https://doi.org/10.1089/end.2019.0475
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