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

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

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 7: Improving Pathological Distribution Measurements with Bayesian Uncertainty
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Citations

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Chapter title
Improving Pathological Distribution Measurements with Bayesian Uncertainty
Chapter number 7
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_7
Book ISBNs
978-3-03-060364-9, 978-3-03-060365-6
Authors

Ka Ho Tam, Korsuk Sirinukunwattana, Maria F. Soares, Maria Kaisar, Rutger Ploeg, Jens Rittscher, Tam, Ka Ho, Sirinukunwattana, Korsuk, Soares, Maria F., Kaisar, Maria, Ploeg, Rutger, Rittscher, Jens

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 50%
Researcher 1 17%
Student > Postgraduate 1 17%
Student > Master 1 17%
Readers by discipline Count As %
Engineering 4 67%
Computer Science 2 33%

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