CYBER DEAL: 50% off all Springer eBooks | Get this offer!

Image Processing, Computer Vision, Pattern Recognition, and Graphics

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

Editors: Sudre, C.H., Fehri, H., Arbel, T., Baumgartner, C.F., Dalca, A., Tanno, R., Van Leemput, K., Wells, W.M., Sotiras, A., Papiez, B., Ferrante, E., Parisot, S. (Eds.)

Free Preview

Buy this book

eBook $54.99
price for Brazil
  • ISBN 978-3-030-60365-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for Brazil
About this book

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.

Table of contents (20 chapters)

Table of contents (20 chapters)
  • Image Registration via Stochastic Gradient Markov Chain Monte Carlo

    Pages 3-12

    Grzech, Daniel (et al.)

  • RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation

    Pages 13-22

    Gantenbein, Marc (et al.)

  • Hierarchical Brain Parcellation with Uncertainty

    Pages 23-31

    Graham, Mark S. (et al.)

  • Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-class Segmentation

    Pages 32-41

    Camarasa, Robin (et al.)

  • Uncertainty Estimation in Landmark Localization Based on Gaussian Heatmaps

    Pages 42-51

    Payer, Christian (et al.)

Buy this book

eBook $54.99
price for Brazil
  • ISBN 978-3-030-60365-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for Brazil
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis
Book Subtitle
Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings
Editors
  • Carole H. Sudre
  • Hamid Fehri
  • Tal Arbel
  • Christian F. Baumgartner
  • Adrian Dalca
  • Ryutaro Tanno
  • Koen Van Leemput
  • William M. Wells
  • Aristeidis Sotiras
  • Bartlomiej Papiez
  • Enzo Ferrante
  • Sarah Parisot
Series Title
Image Processing, Computer Vision, Pattern Recognition, and Graphics
Series Volume
12443
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-60365-6
DOI
10.1007/978-3-030-60365-6
Softcover ISBN
978-3-030-60364-9
Edition Number
1
Number of Pages
XVII, 222
Number of Illustrations
29 b/w illustrations, 75 illustrations in colour
Topics