↓ Skip to main content
What is this page? Embed badge

Head and Neck Tumor Segmentation and Outcome Prediction

Overview of attention for book
Cover of 'Head and Neck Tumor Segmentation and Outcome Prediction'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT
  3. Altmetric Badge
    Chapter 2 Automated Head and Neck Tumor Segmentation from 3D PET/CT HECKTOR 2022 Challenge Report
  4. Altmetric Badge
    Chapter 3 A Coarse-to-Fine Ensembling Framework for Head and Neck Tumor and Lymph Segmentation in CT and PET Images
  5. Altmetric Badge
    Chapter 4 A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images
  6. Altmetric Badge
    Chapter 5 Octree Boundary Transfiner: Efficient Transformers for Tumor Segmentation Refinement
  7. Altmetric Badge
    Chapter 6 Head and Neck Primary Tumor and Lymph Node Auto-segmentation for PET/CT Scans
  8. Altmetric Badge
    Chapter 7 Fusion-Based Automated Segmentation in Head and Neck Cancer via Advance Deep Learning Techniques
  9. Altmetric Badge
    Chapter 8 Stacking Feature Maps of Multi-scaled Medical Images in U-Net for 3D Head and Neck Tumor Segmentation
  10. Altmetric Badge
    Chapter 9 A Fine-Tuned 3D U-Net for Primary Tumor and Affected Lymph Nodes Segmentation in Fused Multimodal Images of Oropharyngeal Cancer
  11. Altmetric Badge
    Chapter 10 A U-Net Convolutional Neural Network with Multiclass Dice Loss for Automated Segmentation of Tumors and Lymph Nodes from Head and Neck Cancer PET/CT Images
  12. Altmetric Badge
    Chapter 11 Multi-scale Fusion Methodologies for Head and Neck Tumor Segmentation
  13. Altmetric Badge
    Chapter 12 Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach
  14. Altmetric Badge
    Chapter 13 Simplicity Is All You Need: Out-of-the-Box nnUNet Followed by Binary-Weighted Radiomic Model for Segmentation and Outcome Prediction in Head and Neck PET/CT
  15. Altmetric Badge
    Chapter 14 Radiomics-Enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer
  16. Altmetric Badge
    Chapter 15 Recurrence-Free Survival Prediction Under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers
  17. Altmetric Badge
    Chapter 16 Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT Images
  18. Altmetric Badge
    Chapter 17 MLC at HECKTOR 2022: The Effect and Importance of Training Data When Analyzing Cases of Head and Neck Tumors Using Machine Learning
  19. Altmetric Badge
    Chapter 18 Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients
  20. Altmetric Badge
    Chapter 19 Combining nnUNet and AutoML for Automatic Head and Neck Tumor Segmentation and Recurrence-Free Survival Prediction in PET/CT Images
  21. Altmetric Badge
    Chapter 20 Head and Neck Cancer Localization with Retina Unet for Automated Segmentation and Time-To-Event Prognosis from PET/CT Images
  22. Altmetric Badge
    Chapter 21 HNT-AI: An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG- PET/CT Images
  23. Altmetric Badge
    Chapter 22 Head and Neck Tumor Segmentation with 3D UNet and Survival Prediction with Multiple Instance Neural Network
  24. Altmetric Badge
    Chapter 23 Deep Learning and Machine Learning Techniques for Automated PET/CT Segmentation and Survival Prediction in Head and Neck Cancer
  25. Altmetric Badge
    Chapter 24 Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients
Attention for Chapter 18: Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

twitter
7 X users

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
2 Mendeley
Summary X Dimensions citations
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients
Chapter number 18
Book title
Head and Neck Tumor Segmentation and Outcome Prediction
Published in
arXiv, January 2023
DOI 10.1007/978-3-031-27420-6_18
Book ISBNs
978-3-03-127419-0, 978-3-03-127420-6
Authors

Juanco-Müller, Ángel Víctor, Mota, João F. C., Goatman, Keith, Hoogendoorn, Corné, Angel Victor Juanco Muller, Joao F. C. Mota, Keith A. Goatman, Corne Hoogendoorn

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 1 50%
Student > Postgraduate 1 50%
Readers by discipline Count As %
Computer Science 2 100%
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 17 May 2023.
All research outputs
#13,798,575
of 24,093,053 outputs
Outputs from arXiv
#204,377
of 1,018,817 outputs
Outputs of similar age
#167,670
of 442,089 outputs
Outputs of similar age from arXiv
#6,284
of 36,466 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,018,817 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 78% 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 442,089 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 61% of its contemporaries.
We're also able to compare this research output to 36,466 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.

This page is provided by Altmetric.