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Machine Learning in Medical Imaging

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

Table of Contents

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    Book Overview
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    Chapter 1 Temporal-Adaptive Graph Convolutional Network for Automated Identification of Major Depressive Disorder Using Resting-State fMRI
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    Chapter 2 Error Attention Interactive Segmentation of Medical Image Through Matting and Fusion
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    Chapter 3 A Novel fMRI Representation Learning Framework with GAN
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    Chapter 4 Semi-supervised Segmentation with Self-training Based on Quality Estimation and Refinement
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    Chapter 5 3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies
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    Chapter 6 Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-scale Generative Adversarial Network
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    Chapter 7 Self-recursive Contextual Network for Unsupervised 3D Medical Image Registration
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    Chapter 8 Automated Tumor Proportion Scoring for Assessment of PD-L1 Expression Based on Multi-Stage Ensemble Strategy
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    Chapter 9 Uncertainty Quantification in Medical Image Segmentation with Normalizing Flows
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    Chapter 10 Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest
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    Chapter 11 A 3D+2D CNN Approach Incorporating Boundary Loss for Stroke Lesion Segmentation
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    Chapter 12 Linking Adolescent Brain MRI to Obesity via Deep Multi-cue Regression Network
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    Chapter 13 Robust Multiple Sclerosis Lesion Inpainting with Edge Prior
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    Chapter 14 Segmentation to Label: Automatic Coronary Artery Labeling from Mask Parcellation
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    Chapter 15 GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes
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    Chapter 16 Anatomy-Aware Cardiac Motion Estimation
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    Chapter 17 Division and Fusion: Rethink Convolutional Kernels for 3D Medical Image Segmentation
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    Chapter 18 LDGAN: Longitudinal-Diagnostic Generative Adversarial Network for Disease Progression Prediction with Missing Structural MRI
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    Chapter 19 Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation
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    Chapter 20 Boundary-Aware Network for Kidney Tumor Segmentation
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    Chapter 21 O-Net: An Overall Convolutional Network for Segmentation Tasks
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    Chapter 22 Label-Driven Brain Deformable Registration Using Structural Similarity and Nonoverlap Constraints
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    Chapter 23 EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis
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    Chapter 24 Deep Distance Map Regression Network with Shape-Aware Loss for Imbalanced Medical Image Segmentation
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    Chapter 25 Joint Appearance-Feature Domain Adaptation: Application to QSM Segmentation Transfer
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    Chapter 26 Exploring Functional Difference Between Gyri and Sulci via Region-Specific 1D Convolutional Neural Networks
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    Chapter 27 Detection of Ischemic Infarct Core in Non-contrast Computed Tomography
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    Chapter 28 Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers
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    Chapter 29 Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients
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    Chapter 30 Structural Connectivity Enriched Functional Brain Network Using Simplex Regression with GraphNet
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    Chapter 31 Constructing High-Order Dynamic Functional Connectivity Networks from Resting-State fMRI for Brain Dementia Identification
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    Chapter 32 Multi-tasking Siamese Networks for Breast Mass Detection Using Dual-View Mammogram Matching
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    Chapter 33 3D Volume Reconstruction from Single Lateral X-Ray Image via Cross-Modal Discrete Embedding Transition
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    Chapter 34 Cleft Volume Estimation and Maxilla Completion Using Cascaded Deep Neural Networks
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    Chapter 35 A Deep Network for Joint Registration and Reconstruction of Images with Pathologies
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    Chapter 36 Learning Conditional Deformable Shape Templates for Brain Anatomy
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    Chapter 37 Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity
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    Chapter 38 Unsupervised Learning for Spherical Surface Registration
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    Chapter 39 Anatomy-Guided Convolutional Neural Network for Motion Correction in Fetal Brain MRI
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    Chapter 40 Gyral Growth Patterns of Macaque Brains Revealed by Scattered Orthogonal Nonnegative Matrix Factorization
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    Chapter 41 Inhomogeneity Correction in Magnetic Resonance Images Using Deep Image Priors
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    Chapter 42 Hierarchical and Robust Pathology Image Reading for High-Throughput Cervical Abnormality Screening
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    Chapter 43 Importance Driven Continual Learning for Segmentation Across Domains
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    Chapter 44 RDCNet: Instance Segmentation with a Minimalist Recurrent Residual Network
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    Chapter 45 Automatic Segmentation of Achilles Tendon Tissues Using Deep Convolutional Neural Network
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    Chapter 46 An End to End System for Measuring Axon Growth
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    Chapter 47 Interwound Structural and Functional Difference Between Preterm and Term Infant Brains Revealed by Multi-view CCA
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    Chapter 48 Graph Convolutional Network Based Point Cloud for Head and Neck Vessel Labeling
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    Chapter 49 Unsupervised Learning-Based Nonrigid Registration of High Resolution Histology Images
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    Chapter 50 Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images
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    Chapter 51 Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation
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    Chapter 52 Gambling Adversarial Nets for Hard Sample Mining and Structured Prediction: Application in Ultrasound Thyroid Nodule Segmentation
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    Chapter 53 Mammographic Image Conversion Between Source and Target Acquisition Systems Using cGAN
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    Chapter 54 An End-to-End Learnable Flow Regularized Model for Brain Tumor Segmentation
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    Chapter 55 Neural Architecture Search for Microscopy Cell Segmentation
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    Chapter 56 Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using Vascular Pattern Detection
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    Chapter 57 Predicting Catheter Ablation Outcomes with Pre-ablation Heart Rhythm Data: Less Is More
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    Chapter 58 AdaBoosted Deep Ensembles: Getting Maximum Performance Out of Small Training Datasets
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    Chapter 59 Cross-Task Representation Learning for Anatomical Landmark Detection
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    Chapter 60 Cycle Ynet: Semi-supervised Tracking of 3D Anatomical Landmarks
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    Chapter 61 Learning Hierarchical Semantic Correspondence and Gland Instance Segmentation
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    Chapter 62 Open-Set Recognition for Skin Lesions Using Dermoscopic Images
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    Chapter 63 End-to-End Coordinate Regression Model with Attention-Guided Mechanism for Landmark Localization in 3D Medical Images
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    Chapter 64 Enhanced MRI Reconstruction Network Using Neural Architecture Search
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    Chapter 65 Learning Invariant Feature Representation to Improve Generalization Across Chest X-Ray Datasets
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    Chapter 66 Noise-Aware Standard-Dose PET Reconstruction Using General and Adaptive Robust Loss
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    Chapter 67 Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation
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    Chapter 68 Informative Feature-Guided Siamese Network for Early Diagnosis of Autism
Attention for Chapter 23: EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis
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Chapter title
EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis
Chapter number 23
Book title
Machine Learning in Medical Imaging
Published by
Springer, Cham, September 2020
DOI 10.1007/978-3-030-59861-7_23
Book ISBNs
978-3-03-059860-0, 978-3-03-059861-7
Authors

Kevin Pan, Guillem Hurault, Kai Arulkumaran, Hywel C. Williams, Reiko J. Tanaka, Pan, Kevin, Hurault, Guillem, Arulkumaran, Kai, Williams, Hywel C., Tanaka, Reiko J.

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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.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 12%
Researcher 3 9%
Other 2 6%
Student > Bachelor 2 6%
Student > Master 2 6%
Other 2 6%
Unknown 19 56%
Readers by discipline Count As %
Computer Science 5 15%
Engineering 4 12%
Unspecified 1 3%
Biochemistry, Genetics and Molecular Biology 1 3%
Mathematics 1 3%
Other 3 9%
Unknown 19 56%

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