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

Brainlesion : Part I / 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. Alessandro Crimi, Spyridon Bakas (eds.).

This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modal...

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Bibliographic Details
Corporate Authors: BrainLes (Workshop), International Conference on Medical Image Computing and Computer-Assisted Intervention
Other Authors: Crimi, Alessandro (Editor), Bakas, Spyridon (Editor)
Format: eBook
Language:English
Published: Cham : Springer, [2022]
Series:Lecture notes in computer science ; 12962.
Subjects:
Brain > Tumors > Congresses.
Brain > Wounds and injuries > Congresses.
Brain > Tumors
Brain > Wounds and injuries
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Online Access:Click for online access
Table of Contents:
  • 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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