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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis

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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical…
Springer International Publishing

Table of Contents

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    Book Overview
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    Chapter 1 Image Registration via Stochastic Gradient Markov Chain Monte Carlo
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    Chapter 2 RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation
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    Chapter 3 Hierarchical Brain Parcellation with Uncertainty
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    Chapter 4 Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-class Segmentation
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    Chapter 5 Uncertainty Estimation in Landmark Localization Based on Gaussian Heatmaps
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    Chapter 6 Weight Averaging Impact on the Uncertainty of Retinal Artery-Venous Segmentation
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    Chapter 7 Improving Pathological Distribution Measurements with Bayesian Uncertainty
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    Chapter 8 Improving Reliability of Clinical Models Using Prediction Calibration
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    Chapter 9 Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior
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    Chapter 10 Uncertainty Estimation for Assessment of 3D US Scan Adequacy and DDH Metric Reliability
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    Chapter 11 Clustering-Based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates
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    Chapter 12 Detection of Discriminative Neurological Circuits Using Hierarchical Graph Convolutional Networks in fMRI Sequences
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    Chapter 13 Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders
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    Chapter 14 Multi-scale Profiling of Brain Multigraphs by Eigen-Based Cross-diffusion and Heat Tracing for Brain State Profiling
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    Chapter 15 Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation
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    Chapter 16 Min-Cut Max-Flow for Network Abnormality Detection: Application to Preterm Birth
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    Chapter 17 Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface
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    Chapter 18 The GraphNet Zoo: An All-in-One Graph Based Deep Semi-supervised Framework for Medical Image Classification
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    Chapter 19 Intraoperative Liver Surface Completion with Graph Convolutional VAE
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    Chapter 20 HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
Attention for Chapter 15: Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation
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Chapter title
Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation
Chapter number 15
Book title
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis
Published by
Springer, Cham, October 2020
DOI 10.1007/978-3-030-60365-6_15
Book ISBNs
978-3-03-060364-9, 978-3-03-060365-6
Authors

Karthik Gopinath, Christian Desrosiers, Herve Lombaert

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X Demographics

The data shown below were collected from the profiles of 10 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 19%
Student > Master 4 15%
Professor 2 7%
Researcher 2 7%
Student > Doctoral Student 1 4%
Other 1 4%
Unknown 12 44%
Readers by discipline Count As %
Computer Science 7 26%
Medicine and Dentistry 2 7%
Engineering 2 7%
Neuroscience 1 4%
Agricultural and Biological Sciences 1 4%
Other 0 0%
Unknown 14 52%

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