Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; First International Alessandro Crimi,Bjoern Menze,Heinz Hand

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A Convolutional Neural Network Approach to Brain Tumor Segmentationges (BRATS’13, BRATS’15) and Ischemic Stroke Lesion Segmentation challenge (ISLES’15) reveal that our approach is among the most accurate in the literature, while also being computationally very efficient.

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https://doi.org/10.1007/978-1-4419-0682-3interest and mapped on the target non-specific modalities through co-registration. These non-overlapping ROIs were considered ground truth for later classification. Voxels were evenly split in training and testing sets for a logistic regression model. The statistical significance of resulting accura

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Macrolides and Interstitial Lung Diseasess respectively. Two different networks were trained one with high grade glioma (HGG) data and other with a combination of high grade and low grade gliomas (LGG). Each network was trained with 35 patients for pre-training and 21 patients for fine tuning. The predictions from the two networks were com

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Stroke Lesion Segmentation Using a Probabilistic Atlas of Cerebral Vascular Territorieson with functional areas and for determining the optimal strategy for patient treatment. Manual labeling of each lesion turns out to be time-intensive and costly, making an automated method desirable. Standard approaches for brain parcellation make use of spatial atlases that represent prior informa