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Image Processing, Computer Vision, Pattern Recognition, and Graphics

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings

Editors: Stoyanov, D., Taylor, Z., Carneiro, G., Syeda-Mahmood, T., Martel, A., Maier-Hein, L., Tavares, J.M.R.S., Bradley, A., Papa, J.P., Belagiannis, V., Nascimento, J.C., Lu, Z., Conjeti, S., Moradi, M., Greenspan, H., Madabhushi, A. (Eds.)

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eBook 55,92 €
price for Spain (gross)
  • ISBN 978-3-030-00889-5
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 68,63 €
price for Spain (gross)
  • ISBN 978-3-030-00888-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018.

The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Table of contents (43 chapters)

Table of contents (43 chapters)
  • UNet++: A Nested U-Net Architecture for Medical Image Segmentation

    Pages 3-11

    Zhou, Zongwei (et al.)

  • Deep Semi-supervised Segmentation with Weight-Averaged Consistency Targets

    Pages 12-19

    Perone, Christian S. (et al.)

  • Handling Missing Annotations for Semantic Segmentation with Deep ConvNets

    Pages 20-28

    Petit, Olivier (et al.)

  • A Unified Framework Integrating Recurrent Fully-Convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data

    Pages 29-37

    Jafari, Mohammad H. (et al.)

  • Multi-scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification

    Pages 38-46

    Peng, Liying (et al.)

Buy this book

eBook 55,92 €
price for Spain (gross)
  • ISBN 978-3-030-00889-5
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 68,63 €
price for Spain (gross)
  • ISBN 978-3-030-00888-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
Book Subtitle
4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings
Editors
  • Danail Stoyanov
  • Zeike Taylor
  • Gustavo Carneiro
  • Tanveer Syeda-Mahmood
  • Anne Martel
  • Lena Maier-Hein
  • João Manuel R. S. Tavares
  • Andrew Bradley
  • Joao Paulo Papa
  • Vasileios Belagiannis
  • Jacinto C. Nascimento
  • Zhi Lu
  • Sailesh Conjeti
  • Mehdi Moradi
  • Hayit Greenspan
  • Anant Madabhushi
Series Title
Image Processing, Computer Vision, Pattern Recognition, and Graphics
Series Volume
11045
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-00889-5
DOI
10.1007/978-3-030-00889-5
Softcover ISBN
978-3-030-00888-8
Edition Number
1
Number of Pages
XVII, 387
Number of Illustrations
159 b/w illustrations
Topics