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,
出版項 : Cham :Springer,[2022]
面頁冊數 : 1 online resource :illustrations (chiefly color).
附註 : International conference proceedings.
附註 : Includes author index.
內容註 : Supervoxel Merging towards Brain Tumor Segmentation -- Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI -- Modeling multi-annotator uncertainty as multi-class segmentation problem -- Modeling multi-annotator uncertainty as multi-class segmentation problem -- Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma -- Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks -- Optimization of Deep Learning based Brain Extraction in MRI for Low Resource Environments. Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task -- Unet3D with Multiple Atrous Convolutions Attention Block for Brain Tumor Segmentation -- BRATS2021: exploring each sequence in multi-modal input for baseline U-net performance -- Automatic Brain Tumor Segmentation using Multi-scale Features and Attention Mechanism -- Simple and Fast Convolutional Neural Network applied to median cross sections for predicting the presence of MGMT promoter methylation in FLAIR MRI scans -- MSViT: Multi Scale Vision Transformer forBiomedical Image Segmentation -- Unsupervised Multimodal -- HarDNet-BTS: A Harmonic Shortcut Network for Brain Tumor Segmentation -- Multimodal Brain Tumor Segmentation Algorithm -- Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images -- Multi-plane UNet++ Ensemble for Glioblastoma Segmentation -- Multimodal Brain Tumor Segmentation using Modified UNet Architecture -- A video data based transfer learning approach for classification of MGMT status in brain tumor MR images -- Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021 -- 3D MRI brain tumour segmentation with autoencoder regularization and Hausdorff distance loss function -- 3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 Challenge -- Cascaded training pipeline for 3D brain tumor segmentation -- nnU-Net with Region-based Training and Loss Ensembles for Brain Tumor Segmentation -- Brain Tumor Segmentation Using Attention Activated U-Net with Positive Mining -- Automatic segmentation of brain tumor using 3D convolutional neural networks -- Hierarchical and Global Modality Interaction for Brain Tumor Segmentation -- Ensemble Outperforms Single Models in Brain Tumor Segmentation -- Brain Tumor Segmentation using UNet-Context Encoding Network -- Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI.
標題 : Brain - Congresses. - Wounds and injuries -
ISBN : 9783031089992
ISBN : 3031089995
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041 $aeng
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245 00$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 I /$cAlessandro Crimi, Spyridon Bakas (eds.).
246 30$aBrainLes 2021
246 30$aMICCAI 2021
264 1$aCham :$bSpringer,$c[2022]
264 4$c�2022
300 $a1 online resource :$billustrations (chiefly color).
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aLecture notes in computer science ;$v12962
500 $aInternational conference proceedings.
500 $aIncludes author index.
505 0 $aSupervoxel Merging towards Brain Tumor Segmentation -- Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI -- Modeling multi-annotator uncertainty as multi-class segmentation problem -- Modeling multi-annotator uncertainty as multi-class segmentation problem -- Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma -- Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks -- Optimization of Deep Learning based Brain Extraction in MRI for Low Resource Environments. Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task -- Unet3D with Multiple Atrous Convolutions Attention Block for Brain Tumor Segmentation -- BRATS2021: exploring each sequence in multi-modal input for baseline U-net performance -- Automatic Brain Tumor Segmentation using Multi-scale Features and Attention Mechanism -- Simple and Fast Convolutional Neural Network applied to median cross sections for predicting the presence of MGMT promoter methylation in FLAIR MRI scans -- MSViT: Multi Scale Vision Transformer forBiomedical Image Segmentation -- Unsupervised Multimodal -- HarDNet-BTS: A Harmonic Shortcut Network for Brain Tumor Segmentation -- Multimodal Brain Tumor Segmentation Algorithm -- Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images -- Multi-plane UNet++ Ensemble for Glioblastoma Segmentation -- Multimodal Brain Tumor Segmentation using Modified UNet Architecture -- A video data based transfer learning approach for classification of MGMT status in brain tumor MR images -- Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021 -- 3D MRI brain tumour segmentation with autoencoder regularization and Hausdorff distance loss function -- 3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 Challenge -- Cascaded training pipeline for 3D brain tumor segmentation -- nnU-Net with Region-based Training and Loss Ensembles for Brain Tumor Segmentation -- Brain Tumor Segmentation Using Attention Activated U-Net with Positive Mining -- Automatic segmentation of brain tumor using 3D convolutional neural networks -- Hierarchical and Global Modality Interaction for Brain Tumor Segmentation -- Ensemble Outperforms Single Models in Brain Tumor Segmentation -- Brain Tumor Segmentation using UNet-Context Encoding Network -- Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI.
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:$tBrainlesion : Part I.$dCham : Springer, 2022$z9783031089985
830 0$aLecture notes in computer science ;$v13576.$389356
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