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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

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Cover of 'Uncertainty for Safe Utilization of Machine Learning in Medical Imaging'

Table of Contents

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    Book Overview
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    Chapter 1 MOrphologically-Aware Jaccard-Based ITerative Optimization (MOJITO) for Consensus Segmentation
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    Chapter 2 Quantification of Predictive Uncertainty via Inference-Time Sampling
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    Chapter 3 How certain are your uncertainties?
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    Chapter 4 On the Pitfalls of Entropy-Based Uncertainty for Multi-class Semi-supervised Segmentation
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    Chapter 5 What Do Untargeted Adversarial Examples Reveal in Medical Image Segmentation?
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    Chapter 6 Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentation
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    Chapter 7 Improving Error Detection in Deep Learning Based Radiotherapy Autocontouring Using Bayesian Uncertainty
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    Chapter 8 Stochastic Weight Perturbations Along the Hessian: A Plug-and-Play Method to Compute Uncertainty
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    Chapter 9 Calibration of Deep Medical Image Classifiers: An Empirical Comparison Using Dermatology and Histopathology Datasets
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    Chapter 10 nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods
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    Chapter 11 Generalized Probabilistic U-Net for Medical Image Segementation
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    Chapter 12 Joint Paraspinal Muscle Segmentation and Inter-rater Labeling Variability Prediction with Multi-task TransUNet
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    Chapter 13 Information Gain Sampling for Active Learning in Medical Image Classification
Attention for Chapter 9: Calibration of Deep Medical Image Classifiers: An Empirical Comparison Using Dermatology and Histopathology Datasets
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Chapter title
Calibration of Deep Medical Image Classifiers: An Empirical Comparison Using Dermatology and Histopathology Datasets
Chapter number 9
Book title
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-16749-2_9
Book ISBNs
978-3-03-116748-5, 978-3-03-116749-2
Authors

Carse, Jacob, Alvarez Olmo, Andres, McKenna, Stephen

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