Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
192
Utgivningsdatum
2019-10-11
Upplaga
1st ed. 2019
Förlag
Springer Nature Switzerland AG
Medarbetare
Dalca, Adrian (red.)/Sudre, Carole H. (red.)/Wells, William M. (red.)/Drechsler, Klaus (red.)/Linguraru, Marius George (red.)
Illustrationer
76 Illustrations, color; 7 Illustrations, black and white; XVII, 192 p. 83 illus., 76 illus. in colo
Dimensioner
234 x 156 x 11 mm
Vikt
304 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030326883
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures (häftad)

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

Häftad Engelska, 2019-10-11
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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.
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Innehållsförteckning

UNSURE 2019: Uncertainty quantification and noise modelling.- Probabilistic Surface Reconstruction with Unknown Correspondence.- Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty.- Propagating Uncertainty Across Cascaded Medical Imaging Tasks For Improved Deep Learning Inference.- Reg R-CNN: Lesion Detection and Grading under Noisy Labels.- Fast Nonparametric Mutual Information based Registration and Uncertainty Estimation.- Quantifying Uncertainty of deep neural networks in skin lesion classification.- UNSURE 2019: Domain shift robustness.- A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data.- Out of distribution detection for intra-operative functional imaging.- CLIP 2019.- A Clinical Measuring Platform for Building the Bridge across the Quantification of Pathological N-cells in Medical Imaging for Studies of Disease.- Spatiotemporal statistical model of anatomical landmarks on a human embryonic brain.- Spaciousness filters for non-contrast CT volume segmentation of the intestine region for emergency ileus diagnosis.- Recovering physiological changes in nasal anatomy with confidence estimates.- Synthesis of Medical Images Using GANs.- DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attention Modules for Colon Glands Segmentation.- Multi-instance deep learning with graph convolutional neural networks for diagnosis of kidney diseases using ultrasound imaging.- Data Augmentation from Sketch.- An automated CNN-based 3D anatomical landmark detection method to facilitate surface-based 3D facial shape analysis.- A Device-independent Novel Statistical Modeling for Cerebral TOF-MRA data Segmentation.- Three-dimensional face reconstruction from uncalibrated photographs: application to early detection of genetic syndromes.