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
LEADER 03730nam 2200433 i 4500
001 48837
003 NhCcYBP
005 20231204121155.6
006 m o d
007 cr cnu---unuuu
008 220726s2022 sz o 101 0 eng d
020 $a9783031090028$q(electronic bk.)
020 $a3031090020$q(electronic bk.)
020 $z9783031090011
020 $z3031090012
024 7 $a10.1007/978-3-031-09002-8$2doi
035 $aebs3334933
040 $aNhCcYBP$cNhCcYBP
041 $aeng
050 4$aRC280.B7$bB73 2022
072 7$aCOM012000$2bisacsh
072 7$aUYQV$2bicssc
072 7$aUYQV$2thema
082 04$a616.99/481$223/eng/20220726
111 2 $aMonte Carlo 2000 Conference$d(2000 :$cLisbon, Portugal)$312759
245 10$aBrainlesion :$bglioma, multiple sclerosis, stroke and traumatic brain injuries : 7th International Workshop, BrainLes 2021, held in conjunction with MICCAI 2021, virtual event, September 27, 2021, revised selected papers.$nPart II /$cAlessandro Crimi, Spyridon Bakas (eds.).
246 3 $aBrainLes 2021
264 1$aCham, Switzerland :$bSpringer,$c2022.
300 $a1 online resource
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
347 $atext file
347 $bPDF
490 1 $aLecture notes in computer science ;$v12963
500 $aIncludes author index.
505 0 $aBiTr-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.
533 $aElectronic reproduction.$bIpswich, MA$nAvailable via World Wide Web.
588 0 $aPrint version record.
650 0$aBrain$xWounds and injuries$vCongresses.$389985
700 1 $aCrimi, Alessandro,$eeditor.$389986
700 1 $aBakas, Spyridon,$eeditor.$389987
710 2 $aEBSCOhost$387894
711 2 $aMonte Carlo 2000 Conference$d(2000 :$cLisbon, Portugal)$312759
776 08$iPrint version:$tBrainlesion : Part II.$dCham : Springer, 2022$z9783031090011
830 0$aLecture notes in computer science ;$v13576.$389356
856 40$uhttps://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3334933$zClick to View (限總院院內)