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Conference UNSURE (Workshop) (1st : 2019 : Shenzhen Shi, China) http://id.loc.gov/authorities/names/nb2019021993
Title Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures : first International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / Hayit Greenspan, Ryutaro Tanno, Marius Erdt and others (eds.).
Imprint Cham, Switzerland : Springer, 2019.

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Conference UNSURE (Workshop) (1st : 2019 : Shenzhen Shi, China) http://id.loc.gov/authorities/names/nb2019021993
Series Lecture notes in computer science ; 11840.
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.
Lecture notes in computer science ; http://id.loc.gov/authorities/names/n42015162 11840.
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics. http://id.loc.gov/authorities/names/no2007037053
Subject Diagnostic imaging -- Data processing -- Congresses.
Artificial intelligence -- Medical applications -- Congresses.
Machine learning -- Congresses.
Alt Name Greenspan, Hayit,
Tanno, Ryutaro,
Erdt, Marius,
CLIP (Workshop) (8th : 2019 : Shenzhen Shi, China), jointly held conference.
International Conference on Medical Image Computing and Computer-Assisted Intervention (22nd : 2019 : Shenzhen Shi, China), jointly held conference.
Add Title UNSURE 2019
CLIP 2019
Description 1 online resource (xvii, 192 pages) : illustrations (some color).
polychrome rdacc http://rdaregistry.info/termList/RDAColourContent/1003
Note International conference proceedings.
Includes author index.
Online resource; title from PDF title page (SpringerLink, viewed October 16, 2019).
Contents Intro; Additional Workshop Editors; Preface; Organization; Preface; Organization; Contents; UNSURE 2019: Uncertainty Quantification and Noise Modelling; Probabilistic Surface Reconstruction with Unknown Correspondence; 1 Introduction; 2 Statistical Shape Models; 2.1 Analytical Posterior Models; 3 Method; 3.1 Approximating the Probabilistic Model; 3.2 Projection-Proposal; 4 Evaluation; 5 Conclusion; References; Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty; 1 Introduction; 2 Methods; 3 Experiments and Results
3.1 Rigid Registration and Parameter Update Range3.2 Affine Multi-modality Registration; 3.3 Deformable Registration and Uncertainty Estimation; 3.4 Evaluation of Uncertainty Estimates; 4 Conclusions and Future Work; References; Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference; 1 Introduction; 2 Methodology: Propagating Uncertainty Across Inference Tasks; 3 Experiments and Results; 3.1 MS T2 Lesion Segmentation/Detection Pipeline; 3.2 Brain Tumour Segmentation Pipeline; 4 Conclusions; References
Reg R-CNN: Lesion Detection and Grading Under Noisy Labels1 Introduction; 2 Methods; 2.1 Regression vs. Classification Training; 2.2 Reg R-CNN and Baseline; 2.3 Evaluation; 3 Experiments; 3.1 Utilized Data Sets; 3.2 Training and Inference Setup; 3.3 Results and Discussion; 4 Conclusion; 5 Outlook; References; Fast Nonparametric Mutual-Information-based Registration and Uncertainty Estimation; 1 Introduction; 2 Generative Model; 3 Optimization; 4 Sampling; 5 Experiments; 6 Discussion; References; Quantifying Uncertainty of Deep Neural Networks in Skin Lesion Classification; 1 Introduction
2 Bayesian Neural Networks3 Quantifying Output Uncertainty; 4 Experiments; 4.1 Uncertainty vs Accuracy; 4.2 The (un)certain Cases; 4.3 The (un)certain Classes; 5 Conclusion; References; UNSURE 2019: Domain Shift Robustness; A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data; Abstract; 1 Introduction; 2 Methods; 2.1 Standard Feature Space; 2.2 Outlier Detector; 3 Experiments; 3.1 MNIST Classification; 3.2 Chest X-ray Lung Field Classification; 4 Discussion and Conclusion; References; Out of Distribution Detection for Intra-operative Functional Imaging
1 Introduction2 Methods; 2.1 Principle of WAIC; 2.2 WAIC Computation with INNs; 2.3 Experiments; 3 Results; 4 Discussion; References; CLIP 2019; A Clinical Measuring Platform for Building the Bridge Across the Quantification of Pathological N-Cells in Medical Imaging for Studies of Disease; Abstract; 1 Introduction; 2 Proposed Platform; 3 Implementation Results; 4 Conclusion; References; Spatiotemporal Statistical Model of Anatomical Landmarks on a Human Embryonic Brain; 1 Introduction; 2 Methods; 2.1 Spatiotemporal Statistical Modeling; 2.2 Prediction of the Missing Landmarks; 3 Experiments
Summary This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 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. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.
ISBN 9783030326890 (electronic bk.)
3030326896 (electronic bk.)
9783030326883 (print)
ISBN/ISSN 10.1007/978-3-030-32689-0
10.1007/978-3-030-32
OCLC # 1123191355