Title 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.)

Published Cham : Springer, 2020

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Description 1 online resource (232 pages) Series Lecture notes in computer science ; 12443 LNCS sublibrary, SL 6, Image processing, computer vision, pattern recognition, and graphics Lecture notes in computer science ; 12443. LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics. 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 Summary 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, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts Notes "Organized as a satellite event of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020) in Lima, Peru, which was held completely virtually due to the COVID-19 pandemic."--Preface International conference proceedings Includes author index 3.2 Epistemic Uncertainty with Bayesian Deep Image Prior Online resource; title from PDF title page (SpringerLink, viewed December 14, 2020) Subject 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 Genre/Form proceedings (reports) Conference papers and proceedings Conference papers and proceedings. Actes de congrès. Form Electronic book Author 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 GRAIL (Workshop) (3rd : 2020 : Online) International Conference on Medical Image Computing and Computer-Assisted Intervention (23rd : 2020 : Online) ISBN 9783030603656 3030603652

Other Titles UNSURE 2020 GRAIL 2020

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