Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
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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 I
Crimi, Alessandro; Bakas, Spyridon
Springer International Publishing AG
07/2022
489
Mole
Inglês
9783031089985
15 a 20 dias
783
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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.
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artificial intelligence;bioinformatics;computer science;computer systems;computer vision;education;image analysis;image processing;image segmentation;learning;machine learning;medical images;neural networks;pattern recognition;segmentation methods;software design;software engineering;software quality;validation;verification and validation;Open Access
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
artificial intelligence;bioinformatics;computer science;computer systems;computer vision;education;image analysis;image processing;image segmentation;learning;machine learning;medical images;neural networks;pattern recognition;segmentation methods;software design;software engineering;software quality;validation;verification and validation;Open Access
