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 II
Bakas, Spyridon; Crimi, Alessandro
Springer International Publishing AG
07/2022
601
Mole
Inglês
9783031090011
15 a 20 dias
949
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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.
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;deep learning;education;image analysis;image processing;image segmentation;learning;machine learning;medical images;neural networks;segmentation methods;software design;software engineering;software quality;validation;verification and validation
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.
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;deep learning;education;image analysis;image processing;image segmentation;learning;machine learning;medical images;neural networks;segmentation methods;software design;software engineering;software quality;validation;verification and validation
