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Medical Image Learning with Limited and Noisy Data

Overview of attention for book
Cover of 'Medical Image Learning with Limited and Noisy Data'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Heatmap Regression for Lesion Detection Using Pointwise Annotations
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    Chapter 2 Partial Annotations for the Segmentation of Large Structures with Low Annotation Cost
  4. Altmetric Badge
    Chapter 3 Abstraction in Pixel-wise Noisy Annotations Can Guide Attention to Improve Prostate Cancer Grade Assessment
  5. Altmetric Badge
    Chapter 4 Meta Pixel Loss Correction for Medical Image Segmentation with Noisy Labels
  6. Altmetric Badge
    Chapter 5 Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction
  7. Altmetric Badge
    Chapter 6 Universal Lesion Detection and Classification Using Limited Data and Weakly-Supervised Self-training
  8. Altmetric Badge
    Chapter 7 BoxShrink: From Bounding Boxes to Segmentation Masks
  9. Altmetric Badge
    Chapter 8 Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis
  10. Altmetric Badge
    Chapter 9 SB-SSL: Slice-Based Self-supervised Transformers for Knee Abnormality Classification from MRI
  11. Altmetric Badge
    Chapter 10 Optimizing Transformations for Contrastive Learning in a Differentiable Framework
  12. Altmetric Badge
    Chapter 11 Stain Based Contrastive Co-training for Histopathological Image Analysis
  13. Altmetric Badge
    Chapter 12 CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification
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    Chapter 13 Real Time Data Augmentation Using Fractional Linear Transformations in Continual Learning
  15. Altmetric Badge
    Chapter 14 DIAGNOSE: Avoiding Out-of-Distribution Data Using Submodular Information Measures
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    Chapter 15 Auto-segmentation of Hip Joints Using MultiPlanar UNet with Transfer Learning
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    Chapter 16 Asymmetry and Architectural Distortion Detection with Limited Mammography Data
  18. Altmetric Badge
    Chapter 17 Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT
  19. Altmetric Badge
    Chapter 18 CVAD: An Anomaly Detector for Medical Images Based on Cascade VAE
  20. Altmetric Badge
    Chapter 19 Visual Field Prediction with Missing and Noisy Data Based on Distance-Based Loss
  21. Altmetric Badge
    Chapter 20 Image Quality Classification for Automated Visual Evaluation of Cervical Precancer
  22. Altmetric Badge
    Chapter 21 A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data
  23. Altmetric Badge
    Chapter 22 Automated Skin Biopsy Analysis with Limited Data
Attention for Chapter 21: A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

twitter
6 X users

Citations

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2 Dimensions

Readers on

mendeley
5 Mendeley
Summary X Dimensions citations
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Chapter title
A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data
Chapter number 21
Book title
Medical Image Learning with Limited and Noisy Data
Published in
arXiv, August 2022
DOI 10.1007/978-3-031-16760-7_21
Book ISBNs
978-3-03-116759-1, 978-3-03-116760-7
Authors

Dongyang Kuang, Craig Michoski, Wenting Li, Rui Guo, Kuang, Dongyang, Michoski, Craig, Li, Wenting, Guo, Rui

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 1 20%
Unknown 4 80%
Readers by discipline Count As %
Engineering 1 20%
Unknown 4 80%
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 14 October 2022.
All research outputs
#14,095,539
of 24,093,053 outputs
Outputs from arXiv
#219,358
of 1,020,419 outputs
Outputs of similar age
#174,081
of 418,194 outputs
Outputs of similar age from arXiv
#7,024
of 35,388 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% 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 done well, scoring higher than 76% 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 418,194 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 35,388 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.

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