Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; Third International Alessandro Crimi,Spyridon Bakas,Mauricio

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誤傳

Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation Using?Holistic Convolutioused as a loss function for training convolutional neural networks (CNN). Although CNNs trained using mean-class Dice score achieve state-of-the-art results on multi-class segmentation, this loss function does neither take advantage of inter-class relationships nor multi-scale information. We argue

構(gòu)想

Overall Survival Time Prediction for High Grade Gliomas Based on Sparse Representation Framework imaging-based survival prediction generally relies on some features guided by clinical experiences, which limits the full utilization of biomedical image. In this paper, we propose a sparse representation-based radiomics framework to predict overall survival (OS) time of HGG. Firstly, we develop a

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導(dǎo)師

Pairwise, Ordinal Outlier Detection of?Traumatic Brain Injuriesa model is challenging to construct. Instead, we utilize region-specific pairwise (person-to-person) comparisons. Each person-region is characterized by a distribution of Fractional Anisotropy and comparisons are made via Median, Mean, Bhattacharya and Kullback-Liebler distances. Additionally, we ex

曲解

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Deep Learning Based Multimodal Brain Tumor Diagnosisto address the challenges of multimodal brain tumor segmentation. The proposed multi-view deep learning framework (MvNet) uses three multi-branch fully-convolutional residual networks (Mb-FCRN) to segment multimodal brain images from different view-point, i.e. slices along x, y, z axis. The three su

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Multimodal Brain Tumor Segmentation Using Ensemble of Forest Methodngle forest, we proposed two stage ensemble method for Multimodal Brain Tumor Segmentation problem. Identification of Tumor region and its sub-regions poses challenge in terms of variations in intensity, location etc. from patient to patient. We identify the initial region of interest (ROI) by linea

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