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

Table of Contents

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   Book Overview
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   Chapter 1 A Review of Medical Federated Learning: Applications in Oncology and Cancer Research
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   Chapter 2 Opportunities and Challenges for Deep Learning in Brain Lesions
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   Chapter 3 EMSViT: Efficient Multi Scale Vision Transformer for Biomedical Image Segmentation
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   Chapter 4 CA-Net: Collaborative Attention Network for Multi-modal Diagnosis of Gliomas
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   Chapter 5 Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI
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   Chapter 6 Small Lesion Segmentation in Brain MRIs with Subpixel Embedding
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   Chapter 7 Unsupervised Multimodal Supervoxel Merging Towards Brain Tumor Segmentation
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   Chapter 8 Evaluating Glioma Growth Predictions as a Forward Ranking Problem
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    Chapter 9 Modeling Multi-annotator Uncertainty as Multi-class Segmentation Problem
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    Chapter 10 Adaptive Unsupervised Learning with Enhanced Feature Representation for Intra-tumor Partitioning and Survival Prediction for Glioblastoma
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    Chapter 11 Predicting Isocitrate Dehydrogenase Mutation Status in Glioma Using Structural Brain Networks and Graph Neural Networks
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    Chapter 12 Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments
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    Chapter 13 Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task
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    Chapter 14 Unet3D with Multiple Atrous Convolutions Attention Block for Brain Tumor Segmentation
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    Chapter 15 BRATS2021: Exploring Each Sequence in Multi-modal Input for Baseline U-net Performance
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    Chapter 16 Combining Global Information with Topological Prior for Brain Tumor Segmentation
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    Chapter 17 Automatic Brain Tumor Segmentation Using Multi-scale Features and Attention Mechanism
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    Chapter 18 Simple and Fast Convolutional Neural Network Applied to Median Cross Sections for Predicting the Presence of MGMT Promoter Methylation in FLAIR MRI Scans
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    Chapter 19 Brain Tumor Segmentation Using Non-local Mask R-CNN and Single Model Ensemble
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    Chapter 20 EfficientNet for Brain-Lesion Classification
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    Chapter 21 HarDNet-BTS: A Harmonic Shortcut Network for Brain Tumor Segmentation
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    Chapter 22 Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
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    Chapter 23 Multi-plane UNet++ Ensemble for Glioblastoma Segmentation
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    Chapter 24 Multimodal Brain Tumor Segmentation Using Modified UNet Architecture
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    Chapter 25 A Video Data Based Transfer Learning Approach for Classification of MGMT Status in Brain Tumor MR Images
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    Chapter 26 Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021
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    Chapter 27 3D MRI Brain Tumour Segmentation with Autoencoder Regularization and Hausdorff Distance Loss Function
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    Chapter 28 3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 Challenge
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    Chapter 29 Multi Modal Fusion for Radiogenomics Classification of Brain Tumor
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    Chapter 30 A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation
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    Chapter 31 Brain Tumor Segmentation Using Neural Network Topology Search
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    Chapter 32 Segmenting Brain Tumors in Multi-modal MRI Scans Using a 3D SegNet Architecture
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    Chapter 33 Residual 3D U-Net with Localization for Brain Tumor Segmentation
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    Chapter 34 A Two-Phase Optimal Mass Transportation Technique for 3D Brain Tumor Detection and Segmentation
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    Chapter 35 Cascaded Training Pipeline for 3D Brain Tumor Segmentation
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    Chapter 36 NnUNet with Region-based Training and Loss Ensembles for Brain Tumor Segmentation
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    Chapter 37 Brain Tumor Segmentation Using Attention Activated U-Net with Positive Mining
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    Chapter 38 Hierarchical and Global Modality Interaction for Brain Tumor Segmentation
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    Chapter 39 Ensemble Outperforms Single Models in Brain Tumor Segmentation
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    Chapter 40 Brain Tumor Segmentation Using UNet-Context Encoding Network
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    Chapter 41 Ensemble CNN Networks for GBM Tumors Segmentation Using Multi-parametric MRI