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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures


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About the Book

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.


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Product Details
  • ISBN-13: 9783030326883
  • Publisher: Springer International Publishing
  • Publisher Imprint: Springer
  • Height: 234 mm
  • No of Pages: 192
  • Spine Width: 11 mm
  • Weight: 354 gr
  • ISBN-10: 3030326888
  • Publisher Date: 11 Oct 2019
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Sub Title: First International Workshop, Unsure 2019, and 8th International Workshop, Clip 2019, Held in Conjunction with Miccai 2019, Shenzhen, China, October
  • Width: 156 mm


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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures
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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures
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