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Bayesian Model Selection for Pathological Neuroimaging Data Applied to White Matter Lesion Segmentation.

, , , , , and . IEEE Trans. Medical Imaging, 34 (10): 2079-2102 (2015)

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Supervised novelty detection in brain tissue classification with an application to white matter hyperintensities., , , , , , , and . Medical Imaging: Image Processing, volume 9784 of SPIE Proceedings, page 978421. SPIE, (2016)Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change., , , , , , , , , and 2 other author(s). Neuroinformatics, 18 (3): 429-449 (2020)Bayesian Model Selection for Pathological Neuroimaging Data Applied to White Matter Lesion Segmentation., , , , , and . IEEE Trans. Medical Imaging, 34 (10): 2079-2102 (2015)Employing Visual Analytics to Aid the Design of White Matter Hyperintensity Classifiers., , , , , , and . MICCAI (2), volume 9901 of Lecture Notes in Computer Science, page 97-105. (2016)Automated detection of periventricular veins on 7 T brain MRI., , , , , and . Medical Imaging: Image Processing, volume 9413 of SPIE Proceedings, page 94132T. SPIE, (2015)MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans., , , , , , , , , and 20 other author(s). Comput. Intell. Neurosci., (2015)Bayesian Model Selection for Pathological Data., , , , , and . MICCAI (1), volume 8673 of Lecture Notes in Computer Science, page 323-330. Springer, (2014)