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  • Conference proceedings
  • © 2020

Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning

Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

Conference proceedings info: DART 2020, DCL 2020.

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Table of contents (20 papers)

  1. Front Matter

    Pages i-xiii
  2. DART 2020

    1. Front Matter

      Pages 1-1
    2. DAPR-Net: Domain Adaptive Predicting-Refinement Network for Retinal Vessel Segmentation

      • Zichun Huang, Hongyan Mao, Ningkang Jiang, Xiaoling Wang
      Pages 13-22
    3. Attention-Guided Deep Domain Adaptation for Brain Dementia Identification with Multi-site Neuroimaging Data

      • Hao Guan, Erkun Yang, Pew-Thian Yap, Dinggang Shen, Mingxia Liu
      Pages 31-40
    4. Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps

      • James Tong, Dwarikanath Mahapatra, Paul Bonnington, Tom Drummond, Zongyuan Ge
      Pages 41-51
    5. Cross-Modality Segmentation by Self-supervised Semantic Alignment in Disentangled Content Space

      • Junlin Yang, Xiaoxiao Li, Daniel Pak, Nicha C. Dvornek, Julius Chapiro, MingDe Lin et al.
      Pages 52-61
    6. Semi-supervised Pathology Segmentation with Disentangled Representations

      • Haochuan Jiang, Agisilaos Chartsias, Xinheng Zhang, Giorgos Papanastasiou, Scott Semple, Mark Dweck et al.
      Pages 62-72
    7. Parts2Whole: Self-supervised Contrastive Learning via Reconstruction

      • Ruibin Feng, Zongwei Zhou, Michael B. Gotway, Jianming Liang
      Pages 85-95
    8. Cross-View Label Transfer in Knee MR Segmentation Using Iterative Context Learning

      • Tong Li, Kai Xuan, Zhong Xue, Lei Chen, Lichi Zhang, Dahong Qian
      Pages 96-105
    9. Continual Class Incremental Learning for CT Thoracic Segmentation

      • Abdelrahman Elskhawy, Aneta Lisowska, Matthias Keicher, Joseph Henry, Paul Thomson, Nassir Navab
      Pages 106-116
    10. First U-Net Layers Contain More Domain Specific Information Than the Last Ones

      • Boris Shirokikh, Ivan Zakazov, Alexey Chernyavskiy, Irina Fedulova, Mikhail Belyaev
      Pages 117-126
  3. DCL 2020

    1. Front Matter

      Pages 127-127
    2. Siloed Federated Learning for Multi-centric Histopathology Datasets

      • Mathieu Andreux, Jean Ogier du Terrail, Constance Beguier, Eric W. Tramel
      Pages 129-139
    3. On the Fairness of Privacy-Preserving Representations in Medical Applications

      • Mhd Hasan Sarhan, Nassir Navab, Abouzar Eslami, Shadi Albarqouni
      Pages 140-149
    4. Inverse Distance Aggregation for Federated Learning with Non-IID Data

      • Yousef Yeganeh, Azade Farshad, Nassir Navab, Shadi Albarqouni
      Pages 150-159
    5. Weight Erosion: An Update Aggregation Scheme for Personalized Collaborative Machine Learning

      • Felix Grimberg, Mary-Anne Hartley, Martin Jaggi, Sai Praneeth Karimireddy
      Pages 160-169
    6. Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers

      • Samuel W. Remedios, John A. Butman, Bennett A. Landman, Dzung L. Pham
      Pages 170-180

Other Volumes

  1. Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning

    Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

About this book

This book constitutes the refereed proceedings of the Second MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the First MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic. 

For DART 2020, 12 full papers were accepted from 18 submissions. They deal with methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical settings by making them robust and consistent across different domains.

For DCL 2020, the 8 papers included in this book were accepted from a total of 12 submissions. They focus 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; where information privacy is a priority; where it is necessary to deliver strong guarantees on the amount and nature of private information that may be revealed by the model as a result of training; and where it's necessary to orchestrate, manage and direct clusters of nodes participating in the same learning task.



Keywords

  • bioinformatics
  • computer networks
  • computer security
  • computer vision
  • deep learning
  • education
  • image analysis
  • image processing
  • image reconstruction
  • image segmentation
  • imaging systems
  • learning
  • machine learning
  • medical images
  • network protocols
  • neural networks
  • pattern recognition
  • segmentation methods

Editors and Affiliations

  • Technical University Munich, Munich, Germany

    Shadi Albarqouni

  • University of Pennsylvania, Philadelphia, USA

    Spyridon Bakas

  • Imperial College London, London, UK

    Konstantinos Kamnitsas

  • King's College London, London, UK

    M. Jorge Cardoso

  • Vanderbilt University, Nashville, USA

    Bennett Landman

  • NVIDIA Ltd., Cambridge, UK

    Wenqi Li

  • NVIDIA GmbH and Johnson & Johnson, Munich, Germany

    Fausto Milletari

  • NVIDIA GmbH, Munich, Germany

    Nicola Rieke

  • NVIDIA Corporation, Bethesda, USA

    Holger Roth, Daguang Xu

  • NVIDIA Corporation, Santa Clara, USA

    Ziyue Xu

Bibliographic Information

Buying options

eBook USD 64.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-60548-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 84.99
Price excludes VAT (USA)