↓ Skip to main content

Ophthalmic Medical Image Analysis

Overview of attention for book
Cover of 'Ophthalmic Medical Image Analysis'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Adjacent Scale Fusion and Corneal Position Embedding for Corneal Ulcer Segmentation
  3. Altmetric Badge
    Chapter 2 Longitudinal Detection of Diabetic Retinopathy Early Severity Grade Changes Using Deep Learning
  4. Altmetric Badge
    Chapter 3 Intra-operative OCT (iOCT) Image Quality Enhancement: A Super-Resolution Approach Using High Quality iOCT 3D Scans
  5. Altmetric Badge
    Chapter 4 Diabetic Retinopathy Detection Based on Weakly Supervised Object Localization and Knowledge Driven Attribute Mining
  6. Altmetric Badge
    Chapter 5 FARGO: A Joint Framework for FAZ and RV Segmentation from OCTA Images
  7. Altmetric Badge
    Chapter 6 CDLRS: Collaborative Deep Learning Model with Joint Regression and Segmentation for Automatic Fovea Localization
  8. Altmetric Badge
    Chapter 7 U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina
  9. Altmetric Badge
    Chapter 8 Radial U-Net: Improving DMEK Graft Detachment Segmentation in Radial AS-OCT Scans
  10. Altmetric Badge
    Chapter 9 Guided Adversarial Adaptation Network for Retinal and Choroidal Layer Segmentation
  11. Altmetric Badge
    Chapter 10 Juvenile Refractive Power Prediction Based on Corneal Curvature and Axial Length via a Domain Knowledge Embedding Network
  12. Altmetric Badge
    Chapter 11 Peripapillary Atrophy Segmentation with Boundary Guidance
  13. Altmetric Badge
    Chapter 12 Are Cardiovascular Risk Scores from Genome and Retinal Image Complementary? A Deep Learning Investigation in a Diabetic Cohort
  14. Altmetric Badge
    Chapter 13 Dual-Branch Attention Network and Atrous Spatial Pyramid Pooling for Diabetic Retinopathy Classification Using Ultra-Widefield Images
  15. Altmetric Badge
    Chapter 14 Self-adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images
  16. Altmetric Badge
    Chapter 15 Multi-modality Images Analysis: A Baseline for Glaucoma Grading via Deep Learning
  17. Altmetric Badge
    Chapter 16 Impact of Data Augmentation on Retinal OCT Image Segmentation for Diabetic Macular Edema Analysis
  18. Altmetric Badge
    Chapter 17 Representation and Reconstruction of Image-Based Structural Patterns of Glaucomatous Defects Using only Two Latent Variables from a Variational Autoencoder
  19. Altmetric Badge
    Chapter 18 Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification
  20. Altmetric Badge
    Chapter 19 Attention Guided Slit Lamp Image Quality Assessment
  21. Altmetric Badge
    Chapter 20 Robust Retinal Vessel Segmentation from a Data Augmentation Perspective
Attention for Chapter 18: Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

twitter
5 X users

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
1 Mendeley
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
Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification
Chapter number 18
Book title
Ophthalmic Medical Image Analysis
Published in
arXiv, September 2021
DOI 10.1007/978-3-030-87000-3_18
Book ISBNs
978-3-03-086999-1, 978-3-03-087000-3
Authors

Madadi, Yeganeh, Seydi, Vahid, Sun, Jian, Chaum, Edward, Yousefi, Siamak, Yeganeh Madadi, Vahid Seydi, Jian Sun, Edward Chaum, Siamak Yousefi

Timeline

Login to access the full chart related to this output.

If you don’t have an account, click here to discover Explorer

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Computer Science 1 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 28 September 2022.
All research outputs
#14,338,684
of 24,093,053 outputs
Outputs from arXiv
#237,404
of 1,020,419 outputs
Outputs of similar age
#194,451
of 421,042 outputs
Outputs of similar age from arXiv
#8,308
of 34,559 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,020,419 research outputs from this source. They receive a mean Attention Score of 4.0. This one has gotten more attention than average, scoring higher than 74% 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 421,042 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 51% of its contemporaries.
We're also able to compare this research output to 34,559 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.