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

Table of Contents

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   Book Overview
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   Chapter 1 BiTr-Unet: A CNN-Transformer Combined Network for MRI Brain Tumor Segmentation
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   Chapter 2 Optimized U-Net for Brain Tumor Segmentation
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   Chapter 3 MS UNet: Multi-scale 3D UNet for Brain Tumor Segmentation
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   Chapter 4 Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI Database
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   Chapter 5 Orthogonal-Nets: A Large Ensemble of 2D Neural Networks for 3D Brain Tumor Segmentation
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   Chapter 6 Feature Learning by Attention and Ensemble with 3D U-Net to Glioma Tumor Segmentation
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   Chapter 7 MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks
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   Chapter 8 Brain Tumor Segmentation with Patch-Based 3D Attention UNet from Multi-parametric MRI
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    Chapter 9 Dice Focal Loss with ResNet-like Encoder-Decoder Architecture in 3D Brain Tumor Segmentation
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    Chapter 10 HNF-Netv2 for Brain Tumor Segmentation Using Multi-modal MR Imaging
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    Chapter 11 Disparity Autoencoders for Multi-class Brain Tumor Segmentation
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    Chapter 12 Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging Using Model Ensembling and Super-resolution
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    Chapter 13 An Ensemble Approach to Automatic Brain Tumor Segmentation
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    Chapter 14 Quality-Aware Model Ensemble for Brain Tumor Segmentation
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    Chapter 15 Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs
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    Chapter 16 Extending nn-UNet for Brain Tumor Segmentation
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    Chapter 17 Generalized Wasserstein Dice Loss, Test-Time Augmentation, and Transformers for the BraTS 2021 Challenge
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    Chapter 18 Coupling nnU-Nets with Expert Knowledge for Accurate Brain Tumor Segmentation from MRI
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    Chapter 19 Deep Learning Based Ensemble Approach for 3D MRI Brain Tumor Segmentation
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    Chapter 20 Prediction of MGMT Methylation Status of Glioblastoma Using Radiomics and Latent Space Shape Features
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    Chapter 21 Combining CNNs with Transformer for Multimodal 3D MRI Brain Tumor Segmentation
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    Chapter 22 Brain Tumor Segmentation Using Deep Infomax
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    Chapter 23 Automatic Brain Tumor Segmentation with a Bridge-Unet Deeply Supervised Enhanced with Downsampling Pooling Combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm
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    Chapter 24 Brain Tumor Segmentation with Self-supervised Enhance Region Post-processing
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    Chapter 25 E 1 D 3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 challenge
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    Chapter 26 Brain Tumor Segmentation from Multiparametric MRI Using a Multi-encoder U-Net Architecture
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    Chapter 27 AttU-NET: Attention U-Net for Brain Tumor Segmentation
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    Chapter 28 Brain Tumor Segmentation in mpMRI Scans (BraTS-2021) Using Models Based on U-Net Architecture
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    Chapter 29 Neural Network Based Brain Tumor Segmentation
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    Chapter 30 Brain Tumor Segmentation (BraTS) Challenge Short Paper: Improving Three-Dimensional Brain Tumor Segmentation Using SegResnet and Hybrid Boundary-Dice Loss
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    Chapter 31 A Deep Learning Approach to Glioblastoma Radiogenomic Classification Using Brain MRI
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    Chapter 32 Radiogenomic Prediction of MGMT Using Deep Learning with Bayesian Optimized Hyperparameters
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    Chapter 33 Comparison of MR Preprocessing Strategies and Sequences for Radiomics-Based MGMT Prediction
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    Chapter 34 FedCostWAvg: A New Averaging for Better Federated Learning
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    Chapter 35 Federated Learning Using Variable Local Training for Brain Tumor Segmentation
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    Chapter 36 Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation
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    Chapter 37 Multi-institutional Travelling Model for Tumor Segmentation in MRI Datasets
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    Chapter 38 Efficient Federated Tumor Segmentation via Normalized Tensor Aggregation and Client Pruning
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    Chapter 39 Federated Learning for Brain Tumor Segmentation Using MRI and Transformers
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    Chapter 40 Adaptive Weight Aggregation in Federated Learning for Brain Tumor Segmentation
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    Chapter 41 A Study on Criteria for Training Collaborator Selection in Federated Learning
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    Chapter 42 Center Dropout: A Simple Method for Speed and Fairness in Federated Learning
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    Chapter 43 Brain Tumor Segmentation Using Two-Stage Convolutional Neural Network for Federated Evaluation
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    Chapter 44 Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An Approach for the CrossMoDA Challenge
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    Chapter 45 Unsupervised Cross-modality Domain Adaptation for Segmenting Vestibular Schwannoma and Cochlea with Data Augmentation and Model Ensemble
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    Chapter 46 Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion
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    Chapter 47 nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical Imaging
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    Chapter 48 Extending Probabilistic U-Net Using MC-Dropout to Quantify Data and Model Uncertainty
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    Chapter 49 Holistic Network for Quantifying Uncertainties in Medical Images
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    Chapter 50 Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Net
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    Chapter 51 Meta-learning for Medical Image Segmentation Uncertainty Quantification
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    Chapter 52 Using Soft Labels to Model Uncertainty in Medical Image Segmentation