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Machine Learning in Clinical Neuroimaging

Overview of attention for book
Cover of 'Machine Learning in Clinical Neuroimaging'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Image-to-Image Translation Between Tau Pathology and Neuronal Metabolism PET in Alzheimer Disease with Multi-domain Contrastive Learning
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    Chapter 2 Multi-shell dMRI Estimation from Single-Shell Data via Deep Learning
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    Chapter 3 A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging
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    Chapter 4 Cross-Attention for Improved Motion Correction in Brain PET
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    Chapter 5 VesselShot: Few-shot Learning for Cerebral Blood Vessel Segmentation
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    Chapter 6 WaveSep: A Flexible Wavelet-Based Approach for Source Separation in Susceptibility Imaging
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    Chapter 7 Joint Estimation of Neural Events and Hemodynamic Response Functions from Task fMRI via Convolutional Neural Networks
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    Chapter 8 Learning Sequential Information in Task-Based fMRI for Synthetic Data Augmentation
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    Chapter 9 Causal Sensitivity Analysis for Hidden Confounding: Modeling the Sex-Specific Role of Diet on the Aging Brain
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    Chapter 10 MixUp Brain-Cortical Augmentations in Self-supervised Learning
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    Chapter 11 Brain Age Prediction Based on Head Computed Tomography Segmentation
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    Chapter 12 Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification
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    Chapter 13 Copy Number Variation Informs fMRI-Based Prediction of Autism Spectrum Disorder
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    Chapter 14 Deep Attention Assisted Multi-resolution Networks for the Segmentation of White Matter Hyperintensities in Postmortem MRI Scans
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    Chapter 15 Stroke Outcome and Evolution Prediction from CT Brain Using a Spatiotemporal Diffusion Autoencoder
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    Chapter 16 Morphological Versus Functional Network Organization: A Comparison Between Structural Covariance Networks and Probabilistic Functional Modes
Attention for Chapter 1: Image-to-Image Translation Between Tau Pathology and Neuronal Metabolism PET in Alzheimer Disease with Multi-domain Contrastive Learning
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

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4 X users

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Summary X
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Chapter title
Image-to-Image Translation Between Tau Pathology and Neuronal Metabolism PET in Alzheimer Disease with Multi-domain Contrastive Learning
Chapter number 1
Book title
Machine Learning in Clinical Neuroimaging
Published in
Lecture notes in computer science, October 2023
DOI 10.1007/978-3-031-44858-4_1
Book ISBNs
978-3-03-144857-7, 978-3-03-144858-4
Authors

Duong, Michael Tran, Das, Sandhitsu R., Khandelwal, Pulkit, Lyu, Xueying, Xie, Long, Yushkevich, Paul A., Wolk, David A., Nasrallah, Ilya M.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 %
Researcher 1 50%
Unknown 1 50%
Readers by discipline Count As %
Unknown 2 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 09 October 2023.
All research outputs
#8,151,688
of 25,936,091 outputs
Outputs from Lecture notes in computer science
#2,340
of 8,176 outputs
Outputs of similar age
#119,343
of 363,208 outputs
Outputs of similar age from Lecture notes in computer science
#8
of 21 outputs
Altmetric has tracked 25,936,091 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 8,176 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has gotten more attention than average, scoring higher than 71% 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 363,208 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 66% of its contemporaries.
We're also able to compare this research output to 21 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 61% of its contemporaries.

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