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Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning

10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Cristina Oyarzun Laura

    1. Fraunhofer IGD, Darmstadt, Germany

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  2. M. Jorge Cardoso

    1. King's College London, London, UK

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  3. Michal Rosen-Zvi

    1. IBM Research-Haifa and Haifa University, Haifa, Israel

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  4. Georgios Kaissis

    1. Technical University of Munich, Munich, Germany

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  5. Marius George Linguraru

    1. Children’s National Health System, Washington, D.C., USA

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  6. Raj Shekhar

    1. Children’s National Health System, Washington, USA

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  7. Stefan Wesarg

    1. Fraunhofer IGD, Darmstadt, Germany

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  8. Marius Erdt

    1. Fraunhofer Singapore, Singapore, Singapore

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  9. Klaus Drechsler

    1. Aachen University of Applied Sciences, Jülich, Germany

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  10. Yufei Chen

    1. Tongji University, Shanghai, China

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  11. Shadi Albarqouni

    1. Helmholtz AI, Neuherberg, Germany

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  12. Spyridon Bakas

    1. University of Pennsylvania, Philadelphia, USA

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  13. Bennett Landman

    1. Vanderbilt University, Nashville, USA

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  14. Nicola Rieke

    1. NVIDIA GmbH, Munich, Germany

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  15. Holger Roth

    1. NVIDIA Corporation, Bethesda, USA

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  16. Xiaoxiao Li

    1. University of British Columbia, Vancouver, Canada

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  17. Daguang Xu

    1. NVIDIA Corporation, Santa Clara, USA

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  18. Maria Gabrani

    1. IBM Research Europe, Rueschlikon, Switzerland

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  19. Ender Konukoglu

    1. Computer Vision Laboratory, Zürich, Switzerland

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  20. Michal Guindy

    1. Assuta Medical Centers Radiology, Aviv-Yafo, Israel

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  21. Daniel Rueckert

    1. Technical University of Munich, Munich, Germany

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  22. Alexander Ziller

    1. Technical University of Munich, Munich, Germany

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  23. Dmitrii Usynin

    1. Technical University of Munich, Munich, Germany

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  24. Jonathan Passerat-Palmbach

    1. Imperial College London, London, UK

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12969)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

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Table of contents (17 papers)

  1. Front Matter

    Pages i-xxv
    Download chapter PDF

  2. CLIP

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Intestine Segmentation with Small Computational Cost for Diagnosis Assistance of Ileus and Intestinal Obstruction

      • Hirohisa Oda, Yuichiro Hayashi, Takayuki Kitasaka, Aitaro Takimoto, Akinari Hinoki, Hiroo Uchida et al.
      Pages 3-12

    3. Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning

      • Austin Tapp, Christian Payer, Jérôme Schmid, Michael Polanco, Isaac Kumi, Sebastian Bawab et al.
      Pages 13-23

    4. Bayesian Graph Neural Networks for EEG-Based Emotion Recognition

      • Jianhui Chen, Hui Qian, Xiaoliang Gong
      Pages 24-33

    5. ViTBIS: Vision Transformer for Biomedical Image Segmentation

      • Abhinav Sagar
      Pages 34-45

    6. Attention-Guided Pancreatic Duct Segmentation from Abdominal CT Volumes

      • Chen Shen, Holger R. Roth, Hayashi Yuichiro, Masahiro Oda, Tadaaki Miyamoto, Gen Sato et al.
      Pages 46-55

    7. Development of the Next Generation Hand-Held Doppler with Waveform Phasicity Predictive Capabilities Using Deep Learning

      • Adrit Rao, Akshay Chaudhari, Oliver Aalami
      Pages 56-67

    8. Learning from Mistakes: An Error-Driven Mechanism to Improve Segmentation Performance Based on Expert Feedback

      • Siri Willems, Heleen Bollen, Julie van der Veen, Edmond Sterpin, Wouter Crijns, Sandra Nuyts et al.
      Pages 68-77

    9. TMJOAI: An Artificial Web-Based Intelligence Tool for Early Diagnosis of the Temporomandibular Joint Osteoarthritis

      • Celia Le, Romain Deleat-Besson, Najla Al Turkestani, Lucia Cevidanes, Jonas Bianchi, Winston Zhang et al.
      Pages 78-87

    10. COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty

      • Masahiro Oda, Tong Zheng, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi et al.
      Pages 88-97

  3. DCL

    1. Front Matter

      Pages 99-99
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    2. Multi-task Federated Learning for Heterogeneous Pancreas Segmentation

      • Chen Shen, Pochuan Wang, Holger R. Roth, Dong Yang, Daguang Xu, Masahiro Oda et al.
      Pages 101-110

    3. Federated Learning in the Cloud for Analysis of Medical Images - Experience with Open Source Frameworks

      • Przemysław Jabłecki, Filip Ślazyk, Maciej Malawski
      Pages 111-119

    4. On the Fairness of Swarm Learning in Skin Lesion Classification

      • Di Fan, Yifan Wu, Xiaoxiao Li
      Pages 120-129

  4. LL-COVID19

    1. Front Matter

      Pages 131-131
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    2. Lessons Learned from the Development and Application of Medical Imaging-Based AI Technologies for Combating COVID-19: Why Discuss, What Next

      • Maria Gabrani, Ender Konukoglu, David Beymer, Gustavo Carneiro, Jannis Born, Michal Guindy et al.
      Pages 133-140

    3. The Role of Pleura and Adipose in Lung Ultrasound AI

      • Gautam Rajendrakumar Gare, Wanwen Chen, Alex Ling Yu Hung, Edward Chen, Hai V. Tran, Tom Fox et al.
      Pages 141-149

    4. DuCN: Dual-Children Network for Medical Diagnosis and Similar Case Recommendation Towards COVID-19

      • Yunfei Long, Senhua Zhu, Dandan Tu
      Pages 150-159

  5. PPML

    1. Front Matter

      Pages 161-161
      Download chapter PDF
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Other volumes

  1. Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning

Keywords

About this book

This book constitutes the refereed proceedings of the 10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, Second MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, First MICCAI Workshop, LL-COVID19, First Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.

CLIP 2021 accepted 9 papers from the 13 submissions received. It focuses on holistic patient models for personalized healthcare with the goal to bring basic research methods closer to the clinical practice.

For DCL 2021, 4 papers from 7 submissions were accepted for publication. They deal with machine learning applied to problems where data cannot be stored in centralized databases and information privacy is a priority.

LL-COVID19 2021 accepted 2 papers out of 3 submissions dealing with the use of AI models in clinical practice.

And for PPML 2021, 2 papers were accepted from a total of 6 submissions, exploring the use of privacy techniques in the medical imaging community.

Editors and Affiliations

  • Fraunhofer IGD, Darmstadt, Germany

    Cristina Oyarzun Laura, Stefan Wesarg

  • King's College London, London, UK

    M. Jorge Cardoso

  • IBM Research-Haifa and Haifa University, Haifa, Israel

    Michal Rosen-Zvi

  • Technical University of Munich, Munich, Germany

    Georgios Kaissis, Daniel Rueckert, Alexander Ziller, Dmitrii Usynin

  • Children’s National Health System, Washington, D.C., USA

    Marius George Linguraru

  • Children’s National Health System, Washington, USA

    Raj Shekhar

  • Fraunhofer Singapore, Singapore, Singapore

    Marius Erdt

  • Aachen University of Applied Sciences, Jülich, Germany

    Klaus Drechsler

  • Tongji University, Shanghai, China

    Yufei Chen

  • Helmholtz AI, Neuherberg, Germany

    Shadi Albarqouni

  • University of Pennsylvania, Philadelphia, USA

    Spyridon Bakas

  • Vanderbilt University, Nashville, USA

    Bennett Landman

  • NVIDIA GmbH, Munich, Germany

    Nicola Rieke

  • NVIDIA Corporation, Bethesda, USA

    Holger Roth

  • University of British Columbia, Vancouver, Canada

    Xiaoxiao Li

  • NVIDIA Corporation, Santa Clara, USA

    Daguang Xu

  • IBM Research Europe, Rueschlikon, Switzerland

    Maria Gabrani

  • Computer Vision Laboratory, Zürich, Switzerland

    Ender Konukoglu

  • Assuta Medical Centers Radiology, Aviv-Yafo, Israel

    Michal Guindy

  • Imperial College London, London, UK

    Jonathan Passerat-Palmbach

Bibliographic Information

  • Book Title: Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning

  • Book Subtitle: 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings

  • Editors: Cristina Oyarzun Laura, M. Jorge Cardoso, Michal Rosen-Zvi, Georgios Kaissis, Marius George Linguraru, Raj Shekhar, Stefan Wesarg, Marius Erdt, Klaus Drechsler, Yufei Chen, Shadi Albarqouni, Spyridon Bakas, Bennett Landman, Nicola Rieke, Holger Roth, Xiaoxiao Li, Daguang Xu, Maria Gabrani, Ender Konukoglu, Michal Guindy, Daniel Rueckert, Alexander Ziller, Dmitrii Usynin, … Jonathan Passerat-Palmbach

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-90874-4

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2021

  • Softcover ISBN: 978-3-030-90873-7Published: 14 November 2021

  • eBook ISBN: 978-3-030-90874-4Published: 13 November 2021

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XXV, 190

  • Number of Illustrations: 11 b/w illustrations, 67 illustrations in colour

  • Topics: Image Processing and Computer Vision, Machine Learning, Computer Communication Networks, Computer Appl. in Social and Behavioral Sciences

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