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Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation.

, , , and . Medical Image Anal., (2020)

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Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation., , , and . MICCAI (1), volume 11070 of Lecture Notes in Computer Science, page 655-663. Springer, (2018)Automated detection of focal cortical dysplasia lesions using computational models of their MRI characteristics and texture analysis., , , , , , , and . NeuroImage, 19 (4): 1748-1759 (2003)Evaluation of automated techniques for the quantification of grey matter atrophy in patients with multiple sclerosis., , , , , , , and . NeuroImage, 52 (4): 1261-1267 (2010)Automated separation of diffusely abnormal white matter from focal white matter lesions on MRI in multiple sclerosis., , , , , and . NeuroImage, (2020)Segmentation-Consistent Probabilistic Lesion Counting., , , and . MIDL, volume 172 of Proceedings of Machine Learning Research, page 1034-1056. PMLR, (2022)Adaptive Voxel, Texture and Temporal Conditional Random Fields for Detection of Gad-Enhancing Multiple Sclerosis Lesions in Brain MRI., , , , and . MICCAI (3), volume 8151 of Lecture Notes in Computer Science, page 543-550. Springer, (2013)Bayesian Classification of Multiple Sclerosis Lesions in Longitudinal MRI Using Subtraction Images., , , , and . MICCAI (2), volume 6362 of Lecture Notes in Computer Science, page 290-297. Springer, (2010)Saliency Based Deep Neural Network for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI., , and . BrainLes@MICCAI (1), volume 11992 of Lecture Notes in Computer Science, page 108-118. Springer, (2019)IMaGe: Iterative Multilevel Probabilistic Graphical Model for Detection and Segmentation of Multiple Sclerosis Lesions in Brain MRI., , , and . IPMI, volume 9123 of Lecture Notes in Computer Science, page 514-526. Springer, (2015)No-reference quality measure in brain MRI images using binary operations, texture and set analysis., , , and . IET Image Processing, 11 (9): 672-684 (2017)