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Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health

Third MICCAI Workshop, DeCaF 2022, and Second MICCAI Workshop, FAIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings

  • Conference proceedings
  • © 2022

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Editors:
  1. Shadi Albarqouni

    1. University of Bonn, Bonn, Germany

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  2. Spyridon Bakas

    1. University of Pennsylvania, Philadelphia, USA

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  3. Sophia Bano

    1. University College London, London, UK

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  4. M. Jorge Cardoso

    1. King’s College London, London, UK

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  5. Bishesh Khanal

    1. NepAl Applied Mathematics and Informatics, Kathmandu, Nepal

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  6. Bennett Landman

    1. Vanderbilt University, Brentwood, USA

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  7. Xiaoxiao Li

    1. University of British Columbia, Vancouver, Canada

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  8. Chen Qin

    1. University of Edinburgh, Edinburgh, UK

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  9. Islem Rekik

    1. Istanbul Technical University, Istanbul, Turkey

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  10. Nicola Rieke

    1. NVIDIA GmbH, Munich, Germany

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  11. Holger Roth

    1. NVIDIA Corporation, Santa Clara, USA

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  12. Debdoot Sheet

    1. Indian Institute of Technology, Kharagpur, India

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  13. Daguang Xu

    1. NVIDIA Corporation, Santa Clara, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13573)

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About this book

This book constitutes the refereed proceedings of the Third MICCAI Workshop on Distributed, Collaborative, and Federated Learning, DeCaF 2022, and the Second MICCAI Workshop on Affordable AI and Healthcare, FAIR 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022. FAIR 2022 was held as a hybrid event.

DeCaF 2022 accepted 14 papers from the 18 submissions received. The workshop aims at creating a scientific discussion focusing on the comparison, evaluation, and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases or where information privacy is a priority.

For FAIR 2022, 4 papers from 9 submissions were accepted for publication. The topics of the accepted submissions focus on deep ultrasound segmentation, portable OCT image quality enhancement, self-attention deep networks and knowledge distillation in low-regime setting.

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Keywords

Table of contents (18 papers)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Distributed, Collaborative, and Federated Learning

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation

      • Chenyu You, Jinlin Xiang, Kun Su, Xiaoran Zhang, Siyuan Dong, John Onofrey et al.
      Pages 3-16

    3. FedAP: Adaptive Personalization in Federated Learning for Non-IID Data

      • Yousef Yeganeh, Azade Farshad, Johann Boschmann, Richard Gaus, Maximilian Frantzen, Nassir Navab
      Pages 17-27

    4. Data Stealing Attack on Medical Images: Is It Safe to Export Networks from Data Lakes?

      • Huiyu Li, Nicholas Ayache, HervĂ© Delingette
      Pages 28-36

    5. Can Collaborative Learning Be Private, Robust and Scalable?

      • Dmitrii Usynin, Helena Klause, Johannes C. Paetzold, Daniel Rueckert, Georgios Kaissis
      Pages 37-46

    6. Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-modal Brain Tumor Segmentation

      • Holger R. Roth, Ali Hatamizadeh, Ziyue Xu, Can Zhao, Wenqi Li, Andriy Myronenko et al.
      Pages 47-57

    7. Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning

      • Chen Shen, Pochuan Wang, Dong Yang, Daguang Xu, Masahiro Oda, Po-Ting Chen et al.
      Pages 58-67

    8. GAN Latent Space Manipulation and Aggregation for Federated Learning in Medical Imaging

      • Matteo Pennisi, Federica Proietto Salanitri, Simone Palazzo, Carmelo Pino, Francesco Rundo, Daniela Giordano et al.
      Pages 68-78

    9. A Specificity-Preserving Generative Model for Federated MRI Translation

      • Onat Dalmaz, Usama Mirza, Gökberk Elmas, Muzaffer Ă–zbey, Salman U. H. Dar, Tolga Çukur
      Pages 79-88

    10. Content-Aware Differential Privacy with Conditional Invertible Neural Networks

      • Malte Tölle, Ullrich Köthe, Florian AndrĂ©, Benjamin Meder, Sandy Engelhardt
      Pages 89-99

    11. DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain

      • Garima Aggarwal, Chun-Yin Huang, Di Fan, Xiaoxiao Li, Zehua Wang
      Pages 100-109

    12. Cluster Based Secure Multi-party Computation in Federated Learning for Histopathology Images

      • Seyedeh Maryam Hosseini, Milad Sikaroudi, Morteza Babaei, Hamid R. Tizhoosh
      Pages 110-118

    13. Towards More Efficient Data Valuation in Healthcare Federated Learning Using Ensembling

      • Sourav Kumar, A. Lakshminarayanan, Ken Chang, Feri Guretno, Ivan Ho Mien, Jayashree Kalpathy-Cramer et al.
      Pages 119-129

    14. Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana

      • Klaus Kades, Jonas Scherer, Maximilian Zenk, Marius Kempf, Klaus Maier-Hein
      Pages 130-140

    15. Towards Sparsified Federated Neuroimaging Models via Weight Pruning

      • Dimitris Stripelis, Umang Gupta, Nikhil Dhinagar, Greg Ver Steeg, Paul M. Thompson, JosĂ© Luis Ambite
      Pages 141-151

  3. Affordable AI and Healthcare

    1. Front Matter

      Pages 153-153
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    2. Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection

      • Kaveri A. Thakoor, Ari Carter, Ge Song, Adam Wax, Omar Moussa, Royce W. S. Chen et al.
      Pages 155-167

    3. Deep Learning-Based Segmentation of Pleural Effusion from Ultrasound Using Coordinate Convolutions

      • Germain Morilhat, Naomi Kifle, Sandra FinesilverSmith, Bram Ruijsink, Vittoria Vergani, Habtamu Tegegne Desita et al.
      Pages 168-177

    4. Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks

      • Rakshith Sathish, Swanand Khare, Debdoot Sheet
      Pages 178-189
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Other volumes

  1. Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health

Editors and Affiliations

  • University of Bonn, Bonn, Germany

    Shadi Albarqouni

  • University of Pennsylvania, Philadelphia, USA

    Spyridon Bakas

  • University College London, London, UK

    Sophia Bano

  • King’s College London, London, UK

    M. Jorge Cardoso

  • NepAl Applied Mathematics and Informatics, Kathmandu, Nepal

    Bishesh Khanal

  • Vanderbilt University, Brentwood, USA

    Bennett Landman

  • University of British Columbia, Vancouver, Canada

    Xiaoxiao Li

  • University of Edinburgh, Edinburgh, UK

    Chen Qin

  • Istanbul Technical University, Istanbul, Turkey

    Islem Rekik

  • NVIDIA GmbH, Munich, Germany

    Nicola Rieke

  • NVIDIA Corporation, Santa Clara, USA

    Holger Roth, Daguang Xu

  • Indian Institute of Technology, Kharagpur, India

    Debdoot Sheet

Bibliographic Information

  • Book Title: Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health

  • Book Subtitle: Third MICCAI Workshop, DeCaF 2022, and Second MICCAI Workshop, FAIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings

  • Editors: Shadi Albarqouni, Spyridon Bakas, Sophia Bano, M. Jorge Cardoso, Bishesh Khanal, Bennett Landman, Xiaoxiao Li, Chen Qin, Islem Rekik, Nicola Rieke, Holger Roth, Debdoot Sheet, … Daguang Xu

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-18523-6

  • Publisher: Springer Cham

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

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Softcover ISBN: 978-3-031-18522-9Published: 09 October 2022

  • eBook ISBN: 978-3-031-18523-6Published: 08 October 2022

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XV, 204

  • Number of Illustrations: 8 b/w illustrations, 60 illustrations in colour

  • Topics: Image Processing and Computer Vision, Artificial Intelligence, Computing Milieux, Computer Applications

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