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Tree-Encoded Conditional Random Fields for Image Synthesis.

, , , and . IPMI, volume 9123 of Lecture Notes in Computer Science, page 733-745. Springer, (2015)

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Improving magnetic resonance resolution with supervised learning., , and . ISBI, page 987-990. IEEE, (2014)Random forest FLAIR reconstruction from T1, T2, and PD-weighted MRI., , , and . ISBI, page 1079-1082. IEEE, (2014)A Supervoxel Based Random Forest Synthesis Framework for Bidirectional MR/CT Synthesis., , , , and . SASHIMI@MICCAI, volume 10557 of Lecture Notes in Computer Science, page 33-40. Springer, (2017)Multi-site Evaluation of a Study-Level Classifier for Mammography Using Deep Learning., , , , and . MICCAI (6), volume 12266 of Lecture Notes in Computer Science, page 211-219. Springer, (2020)Towards validation of robotic surgery training assessment across training platforms., , , , , , , and . IROS, page 2539-2544. IEEE, (2011)Self Super-Resolution for Magnetic Resonance Images., , and . MICCAI (3), volume 9902 of Lecture Notes in Computer Science, page 553-560. (2016)Towards integrating task information in skills assessment for dexterous tasks in surgery and simulation., , , , , , and . ICRA, page 5273-5278. IEEE, (2011)Improving self super resolution in magnetic resonance images., , , , and . Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 10578 of SPIE Proceedings, page 1057814. SPIE, (2018)Multi-output decision trees for lesion segmentation in multiple sclerosis., , , and . Image Processing, volume 9413 of SPIE Proceedings, page 94131C. SPIE, (2015)Magnetic resonance image synthesis through patch regression., , , and . ISBI, page 350-353. IEEE, (2013)