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 /: second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings. (2020)

• Record Type: Book
• Other Titles: UNSURE 2020; GRAIL 2020
• Further Information: Note: Carole H. Sudre, Hamid Fehri et al. (eds.).
• Other Names: Sudre, Carole H; Fehri, Hamid; Arbel, Tal; (Professor of health care engineering), Baumgartner, Christian; Dalca, Adrian V (Adrian Vasile); Tanno, Ryutaro; Van Leemput, Koen; Wells, William M; Sotiras, Aristeidis; Papiez, Bartlomiej; UNSURE (Workshop), 2nd; GRAIL (Workshop), 3rd; International Conference on Medical Image Computing and Computer-Assisted Intervention, 23rd
• 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
• Publisher Details: Cham : Springer
• Publication Date: 2020
• Extent: 1 online resource (232 p.)
• Subjects: 616.07/54; Diagnostic imaging -- Data processing -- Congresses; Artificial intelligence -- Medical applications -- Congresses; Machine learning -- Congresses; Application software; Artificial intelligence; Optical data processing; Pattern perception; Electronic books
• Languages: English
• ISBNs: 9783030603656; 3030603652
• Related ISBNs: 9783030603649; 3030603644
• Notes: Note: Online resource; title from PDF title page (SpringerLink, viewed December 14, 2020).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.