Uncertainty for safe utilization of machine learning in medical imaging, and graphs in biomedical image analysis : second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings / Carole H. Sudre, Hamid Fehri et al. (eds.).

This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020,...

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Bibliographic Details
Corporate Authors:	UNSURE (Workshop) Online), GRAIL (Workshop), International Conference on Medical Image Computing and Computer-Assisted Intervention
Other Authors:	Sudre, Carole H., Fehri, Hamid, Arbel, Tal, Baumgartner, Christian (Professor of health care engineering), Dalca, Adrian V. (Adrian Vasile), Tanno, Ryutaro, Van Leemput, Koen, Wells, William M., Sotiras, Aristeidis, Papiez, Bartlomiej
Format:	eBook
Language:	English
Published:	Cham : Springer, 2020.
Series:	Lecture notes in computer science ; 12443. LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics.
Subjects:	Diagnostic imaging > Data processing > Congresses. Artificial intelligence > Medical applications > Congresses. Machine learning > Congresses. Machine learning Diagnostic imaging > Data processing Artificial intelligence > Medical applications Application software Artificial intelligence Optical data processing Pattern perception proceedings (reports) Conference papers and proceedings Conference papers and proceedings. Actes de congrès.
Online Access:	Click for online access

Table of Contents:

Intro
Additional Volume Editors
Preface UNSURE 2020
Organization
Preface GRAIL 2020
Organization
Contents
UNSURE 2020
Image Registration via Stochastic Gradient Markov Chain Monte Carlo
1 Introduction
2 Registration Model
3 Variational Inference
4 Stochastic Gradient Markov Chain Monte Carlo
5 Experiments
6 Discussion
7 Conclusion
References
RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation
1 Introduction
2 Methods
2.1 PHiSeg
2.2 Reversible Architectures
2.3 RevPHiSeg
3 Experimental Results
3.1 Evaluation Metrics
3.2 Datasets
3.3 Experimental Setup
3.4 Experimental Results
4 Discussion and Conclusion
References
Hierarchical Brain Parcellation with Uncertainty
1 Introduction
2 Methods
2.1 Flat Parcellation
2.2 Hierarchical Parcellation
2.3 Hierarchical Uncertainty
2.4 Architecture and Implementation Details
3 Experiments and Results
3.1 Data
3.2 Experiments
3.3 Results and Discussion
4 Conclusions
References
Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-class Segmentation
1 Introduction
2 Methods
2.1 MC Dropout
2.2 Uncertainty Metrics
2.3 Evaluation
3 Experiments
4 Discussion and Conclusion
References
Uncertainty Estimation in Landmark Localization Based on Gaussian Heatmaps
1 Introduction
2 Heatmap Regression for Dataset-Based Uncertainty
3 Heatmap Fitting for Image-Based Uncertainty
4 Experimental Setup
5 Results and Discussion
6 Conclusion
References
Weight Averaging Impact on the Uncertainty of Retinal Artery-Venous Segmentation
1 Introduction
2 Data
3 Bayesian AV Classification
3.1 Baseline
3.2 Stochastic Weight Averaging
3.3 Stochastic Weight Averaging Gaussian
4 Experiments and Results
4.1 Description of Experiments
4.2 Performance of the Networks
4.3 Conclusions
References
Improving Pathological Distribution Measurements with Bayesian Uncertainty
1 Introduction
2 Method
2.1 Histopathological Measurements
2.2 Uncertainty Estimation
2.3 Datasets
2.4 Tissue Segmentation
3 Experiment Results
4 Conclusion
References
Improving Reliability of Clinical Models Using Prediction Calibration
1 Introduction
2 Prediction Calibration in Deep Models
3 Model Evaluation Using Reliability Plots
4 A New Prediction Calibration Objective
5 Experiments
5.1 Dataset and Problem Description
5.2 Model Design
5.3 Results
6 Conclusions
References
Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior
1 Introduction
2 Related Work
3 Methods
3.1 Aleatoric Uncertainty with Deep Image Prior

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