Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Springer International Publishing (Verlag)
978-3-031-09001-1 (ISBN)
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation.- Optimized U-Net for Brain Tumor Segmentation.- MS UNet: Multi-Scale 3D UNet for Brain Tumor Segmentation.- Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI Database.- Orthogonal-Nets: A large ensemble of 2D neural networks for 3D Brain Tumor Segmentation.- Feature Learning by Attention and Ensemble with 3D U-Net to Glioma Tumor Segmentation.- MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks.- Brain Tumor Segmentation with Patch-based 3D Attention UNet from Multi-parametric MRI.- Dice Focal Loss with ResNet-like Encoder-Decoder architecture in 3D Brain Tumor Segmentation.- HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging.- Disparity Autoencoders for Multi-class Brain Tumor Segmentation.- Disparity Autoencoders for Multi-class Brain Tumor Segmentation.- Disparity Autoencoders for Multi-class BrainTumor Segmentation.- Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging using Model Ensembling and Super-resolution.- Quality-aware Model Ensemble for Brain Tumor Segmentation.- Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs.- An Ensemble Approach to Automatic Brain Tumor Segmentation.- Extending nn-UNet for brain tumor segmentation.- Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge.- Coupling nnU-Nets with Expert Knowledge for Accurate Brain Tumor Segmentation from MRI.- Deep Learning based Ensemble Approach for 3D MRI Brain Tumor Segmentation.- Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features.- bining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation.- Automatic Brain Tumor Segmentation with a Bridge-Unet deeply supervised enhanced with downsampling pooling combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm.
Erscheinungsdatum | 21.07.2022 |
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Reihe/Serie | Lecture Notes in Computer Science |
Zusatzinfo | XXIII, 601 p. 225 illus., 195 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 949 g |
Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
Sozialwissenschaften | |
Schlagworte | Applications • Artificial Intelligence • Bioinformatics • Computer Science • Computer systems • computer vision • conference proceedings • Deep learning • Education • Image Analysis • Image Processing • Image Segmentation • Informatics • learning • machine learning • Medical Images • Neural networks • Research • segmentation methods • Software Design • Software engineering • Software Quality • Validation • Verification and Validation |
ISBN-10 | 3-031-09001-2 / 3031090012 |
ISBN-13 | 978-3-031-09001-1 / 9783031090011 |
Zustand | Neuware |
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