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SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D Convolutional Neural Networks.

, , , , , , , , , and . ISBI, page 839-843. IEEE, (2017)

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Overview of the VISCERAL Challenge at ISBI 2015., , , , , , , , , and 9 other author(s). VISCERAL Challenge@ISBI, volume 1390 of CEUR Workshop Proceedings, page 6-11. CEUR-WS.org, (2015)Machine-Based Rejection of Low-Quality Spectra and Estimation of Brain Tumor Probabilities from Magnetic Resonance Spectroscopic Images., , , , and . Bildverarbeitung für die Medizin, page 31-35. Springer, (2006)Segmentation of Skeleton and Organs in Whole-Body CT Images via Iterative Trilateration., , , , , , and . IEEE Trans. Med. Imaging, 36 (11): 2276-2286 (2017)Probabilistic model for 3D interactive segmentation., , , , , , , , and . Computer Vision and Image Understanding, (2016)Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images., , , , , , and . CoRR, (2018)Cloud-Based Evaluation Framework for Big Data., , , and . Future Internet Assembly, volume 7858 of Lecture Notes in Computer Science, page 104-114. Springer, (2013)Inner and outer coronary vessel wall segmentation from CCTA using an active contour model with machine learning-based 3D voxel context-aware image force., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 978502. SPIE, (2016)Spatio-temporal MRI reconstruction by enforcing local and global regularity via dynamic total variation and nuclear norm minimization., , , , and . ISBI, page 306-309. IEEE, (2016)Uncertainty quantification in brain tumor segmentation using CRFs and random perturbation models., , , , , , and . ISBI, page 428-431. IEEE, (2016)Importance of patient DTI's to accurately model glioma growth using the reaction diffusion equation., , , , and . ISBI, page 1142-1145. IEEE, (2013)