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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Overview of attention for book
Cover of 'Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 A Review of Medical Federated Learning: Applications in Oncology and Cancer Research
  3. Altmetric Badge
    Chapter 2 Opportunities and Challenges for Deep Learning in Brain Lesions
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    Chapter 3 EMSViT: Efficient Multi Scale Vision Transformer for Biomedical Image Segmentation
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    Chapter 4 CA-Net: Collaborative Attention Network for Multi-modal Diagnosis of Gliomas
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    Chapter 5 Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI
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    Chapter 6 Small Lesion Segmentation in Brain MRIs with Subpixel Embedding
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    Chapter 7 Unsupervised Multimodal Supervoxel Merging Towards Brain Tumor Segmentation
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    Chapter 8 Evaluating Glioma Growth Predictions as a Forward Ranking Problem
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    Chapter 9 Modeling Multi-annotator Uncertainty as Multi-class Segmentation Problem
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    Chapter 10 Adaptive Unsupervised Learning with Enhanced Feature Representation for Intra-tumor Partitioning and Survival Prediction for Glioblastoma
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    Chapter 11 Predicting Isocitrate Dehydrogenase Mutation Status in Glioma Using Structural Brain Networks and Graph Neural Networks
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    Chapter 12 Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments
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    Chapter 13 Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task
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    Chapter 14 Unet3D with Multiple Atrous Convolutions Attention Block for Brain Tumor Segmentation
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    Chapter 15 BRATS2021: Exploring Each Sequence in Multi-modal Input for Baseline U-net Performance
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    Chapter 16 Combining Global Information with Topological Prior for Brain Tumor Segmentation
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    Chapter 17 Automatic Brain Tumor Segmentation Using Multi-scale Features and Attention Mechanism
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    Chapter 18 Simple and Fast Convolutional Neural Network Applied to Median Cross Sections for Predicting the Presence of MGMT Promoter Methylation in FLAIR MRI Scans
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    Chapter 19 Brain Tumor Segmentation Using Non-local Mask R-CNN and Single Model Ensemble
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    Chapter 20 EfficientNet for Brain-Lesion Classification
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    Chapter 21 HarDNet-BTS: A Harmonic Shortcut Network for Brain Tumor Segmentation
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    Chapter 22 Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
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    Chapter 23 Multi-plane UNet++ Ensemble for Glioblastoma Segmentation
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    Chapter 24 Multimodal Brain Tumor Segmentation Using Modified UNet Architecture
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    Chapter 25 A Video Data Based Transfer Learning Approach for Classification of MGMT Status in Brain Tumor MR Images
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    Chapter 26 Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021
  28. Altmetric Badge
    Chapter 27 3D MRI Brain Tumour Segmentation with Autoencoder Regularization and Hausdorff Distance Loss Function
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    Chapter 28 3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 Challenge
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    Chapter 29 Multi Modal Fusion for Radiogenomics Classification of Brain Tumor
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    Chapter 30 A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation
  32. Altmetric Badge
    Chapter 31 Brain Tumor Segmentation Using Neural Network Topology Search
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    Chapter 32 Segmenting Brain Tumors in Multi-modal MRI Scans Using a 3D SegNet Architecture
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    Chapter 33 Residual 3D U-Net with Localization for Brain Tumor Segmentation
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    Chapter 34 A Two-Phase Optimal Mass Transportation Technique for 3D Brain Tumor Detection and Segmentation
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    Chapter 35 Cascaded Training Pipeline for 3D Brain Tumor Segmentation
  37. Altmetric Badge
    Chapter 36 NnUNet with Region-based Training and Loss Ensembles for Brain Tumor Segmentation
  38. Altmetric Badge
    Chapter 37 Brain Tumor Segmentation Using Attention Activated U-Net with Positive Mining
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    Chapter 38 Hierarchical and Global Modality Interaction for Brain Tumor Segmentation
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    Chapter 39 Ensemble Outperforms Single Models in Brain Tumor Segmentation
  41. Altmetric Badge
    Chapter 40 Brain Tumor Segmentation Using UNet-Context Encoding Network
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    Chapter 41 Ensemble CNN Networks for GBM Tumors Segmentation Using Multi-parametric MRI
Attention for Chapter 30: A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
3 X users

Citations

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10 Dimensions

Readers on

mendeley
12 Mendeley
Summary X Dimensions citations
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Chapter title
A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation
Chapter number 30
Book title
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Published in
arXiv, July 2022
DOI 10.1007/978-3-031-08999-2_30
Book ISBNs
978-3-03-108998-5, 978-3-03-108999-2
Authors

Camillo Saueressig, Adam Berkley, Reshma Munbodh, Ritambhara Singh, Saueressig, Camillo, Berkley, Adam, Munbodh, Reshma, Singh, Ritambhara

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 17%
Lecturer > Senior Lecturer 1 8%
Other 1 8%
Librarian 1 8%
Student > Bachelor 1 8%
Other 1 8%
Unknown 5 42%
Readers by discipline Count As %
Computer Science 4 33%
Engineering 2 17%
Agricultural and Biological Sciences 1 8%
Unknown 5 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 April 2023.
All research outputs
#14,777,893
of 25,654,566 outputs
Outputs from arXiv
#184,414
of 935,416 outputs
Outputs of similar age
#179,192
of 435,201 outputs
Outputs of similar age from arXiv
#4,981
of 27,468 outputs
Altmetric has tracked 25,654,566 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 935,416 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 435,201 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.
We're also able to compare this research output to 27,468 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.

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