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

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Fully Convolutional Deep Residual Neural Networks for Brain Tumor Segmentation employed here in the setting of brain tumors. Inspired by deep residual networks which won the ImageNet ILSVRC 2015 classification challenge, the FCR-NN combines optimization gains from residual identity mappings with a fully convolutional architecture for image segmentation that efficiently accoun

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Brain Tumor Segmantation Using Random Forest Trained on Iteratively Selected Patientsining the RDF in each iteration some patients are added to the training data using some heuristics approach instead of randomly selected training dataset. Feature extraction and selection were applied to select the most discriminative features for training our Random Decision forest on. The post-pro

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Lifted Auto-Context Forests for Brain Tumour Segmentationt and refined via successive layers of Decision Forests (DFs). Specifically, we make the following contributions: (1) . via an efficient node-splitting criterion based on hold-out estimates, (2) . at a tree-level, thereby yielding shallow discriminative ensembles trained orders of magnitude faster,

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain InjuriesSecond International

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https://doi.org/10.1007/978-3-319-92132-7ality of registration validation and the variety of data being made available. By including addition features such as expert tumour segmentations, the database will appeal to a broader spectrum of image processing researchers and be useful for validating a wider range of techniques for image-guided

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