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Brainlesion :glioma, multiple sclerosis, stroke and traumatic brain injuries : 7th International Workshop, BrainLes 2021, held in conjunction with MICCAI 2021, virtual event, September 27, 2021, revised selected papers.,Part II /
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Brainlesion :glioma, multiple sclerosis, stroke and traumatic brain injuries : 7th International Workshop, BrainLes 2021, held in conjunction with MICCAI 2021, virtual event, September 27, 2021, revised selected papers.,Part II /

紀錄類型 : 書目-語言資料,印刷品: 單行本

其他題名 : BrainLes 2021

其他作者 : Crimi, Alessandro,

團體作者 : Monte Carlo 2000 Conference

出版項 : Cham, Switzerland :Springer,2022.

面頁冊數 : 1 online resource

附註 : Includes author index.

內容註 : 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 Brain Tumor 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.

標題 : Brain - Congresses. - Wounds and injuries -

ISBN : 9783031090028

ISBN : 3031090020

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Brainlesion :glioma, multiple sclerosis, stroke and traumatic brain injuries : 7th International Workshop, BrainLes 2021, held in conjunction with MICCAI 2021, virtual event, September 27, 2021, revised selected papers.Part II /BrainLes 2021Alessandro Crimi, Spyridon Bakas (eds.). - 1 online resource - Lecture notes in computer science ;12963. - Lecture notes in computer science ;13576..

Includes author index.

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 Brain Tumor 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.


Electronic reproduction.
Ipswich, MA





Available via World Wide Web.

ISBN: 9783031090028

Standard No.: 10.1007/978-3-031-09002-8doiSubjects--Topical Terms:

89985
Brain
--Wounds and injuries--Congresses.

LC Class. No.: RC280.B7 / B73 2022

Dewey Class. No.: 616.99/481
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