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Random forest FLAIR reconstruction from T1, T2, and PD-weighted MRI., , , и . ISBI, стр. 1079-1082. IEEE, (2014)Improving magnetic resonance resolution with supervised learning., , и . ISBI, стр. 987-990. IEEE, (2014)A Supervoxel Based Random Forest Synthesis Framework for Bidirectional MR/CT Synthesis., , , , и . SASHIMI@MICCAI, том 10557 из Lecture Notes in Computer Science, стр. 33-40. Springer, (2017)Multi-site Evaluation of a Study-Level Classifier for Mammography Using Deep Learning., , , , и . MICCAI (6), том 12266 из Lecture Notes in Computer Science, стр. 211-219. Springer, (2020)Towards integrating task information in skills assessment for dexterous tasks in surgery and simulation., , , , , , и . ICRA, стр. 5273-5278. IEEE, (2011)Towards validation of robotic surgery training assessment across training platforms., , , , , , , и . IROS, стр. 2539-2544. IEEE, (2011)Self Super-Resolution for Magnetic Resonance Images., , и . MICCAI (3), том 9902 из Lecture Notes in Computer Science, стр. 553-560. (2016)Multi-output decision trees for lesion segmentation in multiple sclerosis., , , и . Image Processing, том 9413 из SPIE Proceedings, стр. 94131C. SPIE, (2015)Improving self super resolution in magnetic resonance images., , , , и . Biomedical Applications in Molecular, Structural, and Functional Imaging, том 10578 из SPIE Proceedings, стр. 1057814. SPIE, (2018)PSACNN: Pulse sequence adaptive fast whole brain segmentation., , , , и . NeuroImage, (2019)