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Comparison of Machine Learning Algorithms and Oversampling Techniques for Urinary Toxicity Prediction After Prostate Cancer Radiotherapy., , , , , , , и . BIBE, стр. 964-971. IEEE, (2019)Towards a Reduced In Silico Model Predicting Biochemical Recurrence After Radiotherapy in Prostate Cancer., , , , , , , , и . IEEE Trans. Biomed. Eng., 68 (9): 2718-2729 (2021)Pseudo-CT Generation for Mri-only Radiotherapy: Comparative Study Between A Generative Adversarial Network, A U-Net Network, A Patch-Based, and an Atlas Based Methods., , , , , , , и . ISBI, стр. 1109-1113. IEEE, (2019)Sensitivity Analysis Of An In Silico Model Of Tumor Growth And Radiation Response., , , , , , и . ISBI, стр. 1497-1500. IEEE, (2019)Comparison of feature selection in radiomics for the prediction of overall survival after radiotherapy for hepatocellular carcinoma., , , , , , и . EMBC, стр. 1667-1670. IEEE, (2020)Organs-at-Risk Contouring on Head CT for RT Planning Using 3D Slicer- A Preliminary Study., , , , , , , и . BIBE, стр. 503-506. IEEE, (2019)Multiple Kernel Learning Applied to the Prediction of Prostate Cancer Recurrence from MRI Radiomic Features., , , , , и . BIBE, стр. 984-988. IEEE, (2019)Quantification of dose uncertainties for the bladder in prostate cancer radiotherapy based on dominant eigenmodes., , , , и . SIPAIM, том 10572 из SPIE Proceedings, стр. 1057214. SPIE, (2017)Towards an integrative computational model for simulating tumor growth and response to radiation therapy., , , , , и . SIPAIM, том 10572 из SPIE Proceedings, стр. 1057216. SPIE, (2017)Statistical Shape Model to Generate a Planning Library for Cervical Adaptive Radiotherapy., , , , , , , , , и 1 other автор(ы). IEEE Trans. Medical Imaging, 38 (2): 406-416 (2019)