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

Nibble

误传

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

构想

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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导师

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

LATHE

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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

hurricane

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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