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Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention

International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings

  • Luping Zhou
  • Nicholas Heller
  • Yiyu Shi
  • Yiming Xiao
  • Raphael Sznitman
  • Veronika Cheplygina
  • Diana Mateus
  • Emanuele Trucco
  • X. Sharon Hu
  • Danny Chen
  • Matthieu Chabanas
  • Hassan Rivaz
  • Ingerid Reinertsen
Conference proceedings LABELS 2019, HAL-MICCAI 2019, CuRIOUS 2019

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11851)

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 11851)

Table of contents

  1. Front Matter
    Pages i-xx
  2. 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS 2019)

    1. Front Matter
      Pages 1-1
    2. Daria Zotova, Aneta Lisowska, Owen Anderson, Vismantas Dilys, Alison O’Neil
      Pages 3-12
    3. Dana Rahbani, Andreas Morel-Forster, Dennis Madsen, Marcel Lüthi, Thomas Vetter
      Pages 13-21
    4. Bin Xie, Xiaoyu He, Shuang Zhao, Yi Li, Juan Su, Xinyu Zhao et al.
      Pages 22-31
    5. Tuo Leng, Qingyu Zhao, Chao Yang, Zhufu Lu, Ehsan Adeli, Kilian M. Pohl
      Pages 32-41
    6. Holger Roth, Ling Zhang, Dong Yang, Fausto Milletari, Ziyue Xu, Xiaosong Wang et al.
      Pages 42-50
    7. Wenhui Lei, Huan Wang, Ran Gu, Shichuan Zhang, Shaoting Zhang, Guotai Wang
      Pages 61-69
    8. Nicholas Heller, Jack Rickman, Christopher Weight, Nikolaos Papanikolopoulos
      Pages 70-77
  3. First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention (HAL-MICCAI 2019)

    1. Front Matter
      Pages 79-79
    2. Fan Wang, Chunhua Deng, Bo Yuan, Chao Chen
      Pages 81-88
    3. Hongjia Li, Sheng Lin, Ning Liu, Caiwen Ding, Yanzhi Wang
      Pages 89-97
    4. Jiahui Guan, Ravi Soni, Dibyajyoti Pati, Gopal Avinash, V. Ratna Saripalli
      Pages 98-105
    5. MohammadHossein AskariHemmat, Sina Honari, Lucas Rouhier, Christian S. Perone, Julien Cohen-Adad, Yvon Savaria et al.
      Pages 115-124
  4. Second International Challenge on Correction of Brainshift with Intra-Operative Ultrasound (CuRIOUS 2019)

    1. Front Matter
      Pages 125-125
    2. Luca Canalini, Jan Klein, Dorothea Miller, Ron Kikinis
      Pages 127-135
    3. David Drobny, Marta Ranzini, Sébastien Ourselin, Tom Vercauteren, Marc Modat
      Pages 136-144
  5. Back Matter
    Pages 153-154

About these proceedings

Introduction

This book constitutes the refereed joint proceedings of the 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2019, the First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019.

The 8 papers presented at LABELS 2019, the 5 papers presented at HAL-MICCAI 2019, and the 3 papers presented at CuRIOUS 2019 were carefully reviewed and selected from numerous submissions. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing. The HAL-MICCAI papers cover a wide set of hardware applications in medical problems, including medical image segmentation, electron tomography, pneumonia detection, etc. The CuRIOUS papers provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their image registration methods on newly released standardized datasets of iUS-guided brain tumor resection.


Keywords

artificial intelligence deep learning hardware-aided diagnosis hardware-assisted intervention image analysis image compression image quality image segmentation medical images medical imaging neural networks

Editors and affiliations

  1. 1.University of SydneySydneyAustralia
  2. 2.University of MinnesotaMinneapolisUSA
  3. 3.University of Notre DameNotre DameUSA
  4. 4.Western UniversityLondonCanada
  5. 5.University of BernBernSwitzerland
  6. 6.Eindhoven University of TechnologyEindhovenThe Netherlands
  7. 7.École Centrale de NantesNantesFrance
  8. 8.University of DundeeDundeeUK
  9. 9.University of Notre DameNotre DameUSA
  10. 10.University of Notre DameNotre DameUSA
  11. 11.University of Grenoble AlpesGrenobleFrance
  12. 12.Concordia UniversityMontréalCanada
  13. 13.Health ResearchSINTEF DigitalTrondheimNorway

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-33642-4
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-33641-7
  • Online ISBN 978-3-030-33642-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site