Image Processing, Computer Vision, Pattern Recognition, and Graphics
cover

Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology

Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

Editors: Kia, S.M., Mohy-ud-Din, H., Abdulkadir, A., Bass, C., Habes, M., Rondina, J.M., Tax, C., Wang, H., Wolfers, Th., Rathore, S., Ingalhalikar, M. (Eds.)

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eBook $59.99
price for USA in USD
  • ISBN 978-3-030-66843-3
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
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  • Immediate eBook download after purchase
Softcover $79.99
price for USA in USD
  • ISBN 978-3-030-66842-6
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  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions & severe weather in the US may cause delays
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
About this book

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.*

For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging.

For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience.

*The workshops were held virtually due to the COVID-19 pandemic.

Table of contents (29 chapters)

Table of contents (29 chapters)
  • Surface Agnostic Metrics for Cortical Volume Segmentation and Regression

    Pages 3-12

    Budd, Samuel (et al.)

  • Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain Anatomy

    Pages 13-22

    Amorosino, Gabriele (et al.)

  • Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows

    Pages 23-33

    Wilms, Matthias (et al.)

  • A Multi-task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity

    Pages 34-44

    Nandakumar, Naresh (et al.)

  • Deep Learning for Non-invasive Cortical Potential Imaging

    Pages 45-55

    Razorenova, Alexandra (et al.)

Buy this book

eBook $59.99
price for USA in USD
  • ISBN 978-3-030-66843-3
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $79.99
price for USA in USD
  • ISBN 978-3-030-66842-6
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions & severe weather in the US may cause delays
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology
Book Subtitle
Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings
Editors
  • Seyed Mostafa Kia
  • Hassan Mohy-ud-Din
  • Ahmed Abdulkadir
  • Cher Bass
  • Mohamad Habes
  • Jane Maryam Rondina
  • Chantal Tax
  • Hongzhi Wang
  • Thomas Wolfers
  • Saima Rathore
  • Madhura Ingalhalikar
Series Title
Image Processing, Computer Vision, Pattern Recognition, and Graphics
Series Volume
12449
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-66843-3
DOI
10.1007/978-3-030-66843-3
Softcover ISBN
978-3-030-66842-6
Edition Number
1
Number of Pages
XVIII, 305
Number of Illustrations
8 b/w illustrations
Topics