Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Table of Contents

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   Book Overview
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   Chapter 1 Brain Tumor Segmentation Using Attention-Based Network in 3D MRI Images
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   Chapter 2 Multimodal Brain Image Segmentation and Analysis with Neuromorphic Attention-Based Learning
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   Chapter 3 Improving Brain Tumor Segmentation in Multi-sequence MR Images Using Cross-Sequence MR Image Generation
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   Chapter 4 Ensemble of CNNs for Segmentation of Glioma Sub-regions with Survival Prediction
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   Chapter 5 Brain Tumor Segmentation Based on Attention Mechanism and Multi-model Fusion
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   Chapter 6 Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction
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   Chapter 7 Multimodal Brain Tumor Segmentation and Survival Prediction Using Hybrid Machine Learning
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   Chapter 8 Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs
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    Chapter 9 Brain Tumor Segmentation with Cascaded Deep Convolutional Neural Network
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    Chapter 10 Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas Using Deep Learning and MRI
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    Chapter 11 3D Automatic Brain Tumor Segmentation Using a Multiscale Input U-Net Network
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    Chapter 12 Semi-supervised Variational Autoencoder for Survival Prediction
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    Chapter 13 Multi-modal U-Nets with Boundary Loss and Pre-training for Brain Tumor Segmentation
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    Chapter 14 Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation
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    Chapter 15 Hybrid Labels for Brain Tumor Segmentation
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    Chapter 16 Two Stages CNN-Based Segmentation of Gliomas, Uncertainty Quantification and Prediction of Overall Patient Survival
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    Chapter 17 Detection and Segmentation of Brain Tumors from MRI Using U-Nets
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    Chapter 18 Multimodal Segmentation with MGF-Net and the Focal Tversky Loss Function
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    Chapter 19 Brain Tumor Segmentation Using 3D Convolutional Neural Network
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    Chapter 20 DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation
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    Chapter 21 Brain Tumor Segmentation Based on 3D Residual U-Net
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    Chapter 22 Automatic Segmentation of Brain Tumor from 3D MR Images Using SegNet, U-Net, and PSP-Net
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    Chapter 23 3D Deep Residual Encoder-Decoder CNNS with Squeeze-and-Excitation for Brain Tumor Segmentation
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    Chapter 24 Overall Survival Prediction Using Conventional MRI Features
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    Chapter 25 A Multi-path Decoder Network for Brain Tumor Segmentation
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    Chapter 26 The Tumor Mix-Up in 3D Unet for Glioma Segmentation
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    Chapter 27 Multi-branch Learning Framework with Different Receptive Fields Ensemble for Brain Tumor Segmentation
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    Chapter 28 Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction
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    Chapter 29 Encoder-Decoder Network for Brain Tumor Segmentation on Multi-sequence MRI
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    Chapter 30 Deep Convolutional Neural Networks for Brain Tumor Segmentation: Boosting Performance Using Deep Transfer Learning: Preliminary Results
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    Chapter 31 Multimodal Brain Tumor Segmentation with Normal Appearance Autoencoder
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    Chapter 32 Knowledge Distillation for Brain Tumor Segmentation
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    Chapter 33 Brain Tumor Classification Using 3D Convolutional Neural Network
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    Chapter 34 Brain Tumor Classification with Multimodal MR and Pathology Images
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    Chapter 35 Automatic Classification of Brain Tumor Types with the MRI Scans and Histopathology Images
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    Chapter 36 Brain Tumor Classification with Tumor Segmentations and a Dual Path Residual Convolutional Neural Network from MRI and Pathology Images
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    Chapter 37 From Whole Slide Tissues to Knowledge: Mapping Sub-cellular Morphology of Cancer
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    Chapter 38 The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview