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Machine Learning in Medical Imaging

Overview of attention for book
Cover of 'Machine Learning in Medical Imaging'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Developing Novel Weighted Correlation Kernels for Convolutional Neural Networks to Extract Hierarchical Functional Connectivities from fMRI for Disease Diagnosis
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    Chapter 2 Robust Contextual Bandit via the Capped- $$\ell _{2}$$ ℓ 2 Norm for Mobile Health Intervention
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    Chapter 3 Dynamic Multi-scale CNN Forest Learning for Automatic Cervical Cancer Segmentation
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    Chapter 4 Multi-task Fundus Image Quality Assessment via Transfer Learning and Landmarks Detection
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    Chapter 5 End-to-End Lung Nodule Detection in Computed Tomography
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    Chapter 6 CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
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    Chapter 7 Deep Learning Based Inter-modality Image Registration Supervised by Intra-modality Similarity
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    Chapter 8 Regional Abnormality Representation Learning in Structural MRI for AD/MCI Diagnosis
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    Chapter 9 Joint Registration And Segmentation Of Xray Images Using Generative Adversarial Networks
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    Chapter 10 SCCA-Ref: Novel Sparse Canonical Correlation Analysis with Reference to Discover Independent Spatial Associations Between White Matter Hyperintensities and Atrophy
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    Chapter 11 Synthesizing Dynamic MRI Using Long-Term Recurrent Convolutional Networks
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    Chapter 12 Automatically Designing CNN Architectures for Medical Image Segmentation
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    Chapter 13 Rotation Invariance and Directional Sensitivity: Spherical Harmonics versus Radiomics Features
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    Chapter 14 Can Dilated Convolutions Capture Ultrasound Video Dynamics?
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    Chapter 15 Topological Correction of Infant Cortical Surfaces Using Anatomically Constrained U-Net
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    Chapter 16 Self-taught Learning with Residual Sparse Autoencoders for HEp-2 Cell Staining Pattern Recognition
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    Chapter 17 Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-Ray Segmentation
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    Chapter 18 Brain Status Prediction with Non-negative Projective Dictionary Learning
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    Chapter 19 Classification of Pancreatic Cystic Neoplasms Based on Multimodality Images
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    Chapter 20 Retinal Blood Vessel Segmentation Using a Fully Convolutional Network – Transfer Learning from Patch- to Image-Level
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    Chapter 21 Combining Deep Learning and Active Contours Opens The Way to Robust, Automated Analysis of Brain Cytoarchitectonics
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    Chapter 22 Latent3DU-net: Multi-level Latent Shape Space Constrained 3D U-net for Automatic Segmentation of the Proximal Femur from Radial MRI of the Hip
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    Chapter 23 Adversarial Image Registration with Application for MR and TRUS Image Fusion
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    Chapter 24 Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset
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    Chapter 25 Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks
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    Chapter 26 Ensemble of Multi-sized FCNs to Improve White Matter Lesion Segmentation
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    Chapter 27 Automatic Accurate Infant Cerebellar Tissue Segmentation with Densely Connected Convolutional Network
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    Chapter 28 Nuclei Detection Using Mixture Density Networks
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    Chapter 29 Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs
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    Chapter 30 Graph of Hippocampal Subfields Grading for Alzheimer’s Disease Prediction
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    Chapter 31 Deep Multiscale Convolutional Feature Learning for Weakly Supervised Localization of Chest Pathologies in X-ray Images
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    Chapter 32 Combining Heterogeneously Labeled Datasets For Training Segmentation Networks
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    Chapter 33 SoLiD: Segmentation of Clostridioides Difficile Cells in the Presence of Inhomogeneous Illumination Using a Deep Adversarial Network
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    Chapter 34 On the Adaptability of Unsupervised CNN-Based Deformable Image Registration to Unseen Image Domains
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    Chapter 35 Early Diagnosis of Autism Disease by Multi-channel CNNs
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    Chapter 36 Longitudinal and Multi-modal Data Learning via Joint Embedding and Sparse Regression for Parkinson’s Disease Diagnosis
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    Chapter 37 Prostate Cancer Classification on VERDICT DW-MRI Using Convolutional Neural Networks
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    Chapter 38 Detection of the Pharyngeal Phase in the Videofluoroscopic Swallowing Study Using Inflated 3D Convolutional Networks
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    Chapter 39 End-To-End Alzheimer’s Disease Diagnosis and Biomarker Identification
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    Chapter 40 Small Organ Segmentation in Whole-Body MRI Using a Two-Stage FCN and Weighting Schemes
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    Chapter 41 Masseter Segmentation from Computed Tomography Using Feature-Enhanced Nested Residual Neural Network
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    Chapter 42 Iterative Interaction Training for Segmentation Editing Networks
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    Chapter 43 Temporal Consistent 2D-3D Registration of Lateral Cephalograms and Cone-Beam Computed Tomography Images
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    Chapter 44 Computation of Total Kidney Volume from CT Images in Autosomal Dominant Polycystic Kidney Disease Using Multi-task 3D Convolutional Neural Networks
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    Chapter 45 Dynamic Routing on Deep Neural Network for Thoracic Disease Classification and Sensitive Area Localization
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    Chapter 46 Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Undersampled Data in Magnetic Resonance Fingerprinting (MRF)
Attention for Chapter 39: End-To-End Alzheimer’s Disease Diagnosis and Biomarker Identification
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

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1 news outlet
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4 X users

Citations

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11 Dimensions

Readers on

mendeley
105 Mendeley
Summary News X Dimensions citations
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Chapter title
End-To-End Alzheimer’s Disease Diagnosis and Biomarker Identification
Chapter number 39
Book title
Machine Learning in Medical Imaging
Published in
arXiv, September 2018
DOI 10.1007/978-3-030-00919-9_39
Pubmed ID
32832936
Book ISBNs
978-3-03-000918-2, 978-3-03-000919-9
Authors

Soheil Esmaeilzadeh, Dimitrios Ioannis Belivanis, Kilian M. Pohl, Ehsan Adeli, Esmaeilzadeh, Soheil, Belivanis, Dimitrios Ioannis, Pohl, Kilian M., Adeli, Ehsan

View on publisher site
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 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 105 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 26 25%
Student > Ph. D. Student 15 14%
Researcher 9 9%
Student > Bachelor 9 9%
Student > Doctoral Student 6 6%
Other 14 13%
Unknown 26 25%
Readers by discipline Count As %
Computer Science 36 34%
Neuroscience 10 10%
Engineering 9 9%
Medicine and Dentistry 5 5%
Biochemistry, Genetics and Molecular Biology 3 3%
Other 9 9%
Unknown 33 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 August 2020.
All research outputs
#3,098,191
of 24,002,307 outputs
Outputs from arXiv
#57,216
of 1,011,770 outputs
Outputs of similar age
#58,294
of 314,636 outputs
Outputs of similar age from arXiv
#1,458
of 24,554 outputs
Altmetric has tracked 24,002,307 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,011,770 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 94% 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 314,636 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 24,554 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.

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