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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures

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Cover of 'Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures'

Table of Contents

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    Book Overview
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    Chapter 1 Probabilistic Surface Reconstruction with Unknown Correspondence
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    Chapter 2 Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty
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    Chapter 3 Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference
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    Chapter 4 Reg R-CNN: Lesion Detection and Grading Under Noisy Labels
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    Chapter 5 Fast Nonparametric Mutual-Information-based Registration and Uncertainty Estimation
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    Chapter 6 Quantifying Uncertainty of Deep Neural Networks in Skin Lesion Classification
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    Chapter 7 A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data
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    Chapter 8 Out of Distribution Detection for Intra-operative Functional Imaging
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    Chapter 9 A Clinical Measuring Platform for Building the Bridge Across the Quantification of Pathological N-Cells in Medical Imaging for Studies of Disease
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    Chapter 10 Spatiotemporal Statistical Model of Anatomical Landmarks on a Human Embryonic Brain
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    Chapter 11 Spaciousness Filters for Non-contrast CT Volume Segmentation of the Intestine Region for Emergency Ileus Diagnosis
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    Chapter 12 Recovering Physiological Changes in Nasal Anatomy with Confidence Estimates
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    Chapter 13 Synthesis of Medical Images Using GANs
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    Chapter 14 DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attention Modules for Colon Glands Segmentation
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    Chapter 15 Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging
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    Chapter 16 Data Augmentation from Sketch
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    Chapter 17 An Automated CNN-based 3D Anatomical Landmark Detection Method to Facilitate Surface-Based 3D Facial Shape Analysis
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    Chapter 18 A Device-Independent Novel Statistical Modeling for Cerebral TOF-MRA Data Segmentation
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    Chapter 19 Three-Dimensional Face Reconstruction from Uncalibrated Photographs: Application to Early Detection of Genetic Syndromes
Overall attention for this book and its chapters
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Mentioned by

2 news outlets
1 blog
17 tweeters


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Readers on

24 Mendeley
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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures
Published by
Springer International Publishing, January 2019
DOI 10.1007/978-3-030-32689-0
978-3-03-032688-3, 978-3-03-032689-0

Hayit Greenspan, Ryutaro Tanno, Marius Erdt, Tal Arbel, Christian Baumgartner, Adrian Dalca, Carole H. Sudre, William M. Wells, Klaus Drechsler, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, Miguel Ángel González Ballester

Twitter Demographics

The data shown below were collected from the profiles of 17 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 33%
Student > Master 4 17%
Other 2 8%
Student > Doctoral Student 1 4%
Student > Postgraduate 1 4%
Other 0 0%
Unknown 8 33%
Readers by discipline Count As %
Computer Science 7 29%
Engineering 5 21%
Agricultural and Biological Sciences 2 8%
Medicine and Dentistry 1 4%
Unknown 9 38%