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Domain Adaptation and Representation Transfer

5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Lisa Koch

    1. CONICET/Universidad Nacional del Litoral, Tübingen, Germany

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  2. M. Jorge Cardoso

    1. King’s College London, London, UK

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  3. Enzo Ferrante

    1. CONICET / Universidad Nacional del Litoral, Santa Fe, Argentina

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  4. Konstantinos Kamnitsas

    1. University of Oxford, Oxford, UK

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  5. Mobarakol Islam

    1. Imperial College London, London, UK

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  6. Meirui Jiang

    1. Chinese University of Hong Kong, Hong Kong, Hong Kong

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  7. Nicola Rieke

    1. Nvidia GmbH, Munich, Germany

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  8. Sotirios A. Tsaftaris

    1. University of Edinburgh, Edinburgh, UK

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  9. Dong Yang

    1. Nvidia (United States), Santa Clara, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14293)

Included in the following conference series:

Conference proceedings info: DART 2023.

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About this book

This book constitutes the refereed proceedings of the 5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023, which was held in conjunction with MICCAI 2023, in October 2023. 
The 16 full papers presented in this book were carefully reviewed and selected from 32 submissions. They discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains.

 


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Table of contents (16 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Domain Adaptation of MRI Scanners as an Alternative to MRI Harmonization

    • Rafsanjany Kushol, Richard Frayne, Simon J. Graham, Alan H. Wilman, Sanjay Kalra, Yee-Hong Yang
    Pages 1-11

  3. MultiVT: Multiple-Task Framework for Dentistry

    • Edoardo Mello Rella, Ajad Chhatkuli, Ender Konukoglu, Luc Van Gool
    Pages 12-21

  4. Black-Box Unsupervised Domain Adaptation for Medical Image Segmentation

    • Satoshi Kondo
    Pages 22-30

  5. PLST: A Pseudo-labels with a Smooth Transition Strategy for Medical Site Adaptation

    • Tomer Bar Natan, Hayit Greenspan, Jacob Goldberger
    Pages 31-40

  6. Compositional Representation Learning for Brain Tumour Segmentation

    • Xiao Liu, Antanas Kascenas, Hannah Watson, Sotirios A. Tsaftaris, Alison Q. O’Neil
    Pages 41-51

  7. Hierarchical Compositionality in Hyperbolic Space for Robust Medical Image Segmentation

    • Ainkaran Santhirasekaram, Mathias Winkler, Andrea Rockall, Ben Glocker
    Pages 52-62

  8. Realistic Data Enrichment for Robust Image Segmentation in Histopathology

    • Sarah Cechnicka, James Ball, Hadrien Reynaud, Callum Arthurs, Candice Roufosse, Bernhard Kainz
    Pages 63-72

  9. Boosting Knowledge Distillation via Random Fourier Features for Prostate Cancer Grading in Histopathology Images

    • Trinh Thi Le Vuong, Jin Tae Kwak
    Pages 73-83

  10. Semi-supervised Domain Adaptation for Automatic Quality Control of FLAIR MRIs in a Clinical Data Warehouse

    • Sophie Loizillon, Olivier Colliot, Lydia Chougar, Sebastian Stroer, Yannick Jacob, Aurélien Maire et al.
    Pages 84-93

  11. Towards Foundation Models Learned from Anatomy in Medical Imaging via Self-supervision

    • Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, Jianming Liang
    Pages 94-104

  12. The Performance of Transferability Metrics Does Not Translate to Medical Tasks

    • Levy Chaves, Alceu Bissoto, Eduardo Valle, Sandra Avila
    Pages 105-114

  13. DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy Classification

    • Aleksandr Matsun, Dana O. Mohamed, Sharon Chokuwa, Muhammad Ridzuan, Mohammad Yaqub
    Pages 115-125

  14. SEDA: Self-ensembling ViT with Defensive Distillation and Adversarial Training for Robust Chest X-Rays Classification

    • Raza Imam, Ibrahim Almakky, Salma Alrashdi, Baketah Alrashdi, Mohammad Yaqub
    Pages 126-135

  15. A Continual Learning Approach for Cross-Domain White Blood Cell Classification

    • Ario Sadafi, Raheleh Salehi, Armin Gruber, Sayedali Shetab Boushehri, Pascal Giehr, Nassir Navab et al.
    Pages 136-146

  16. Metadata Improves Segmentation Through Multitasking Elicitation

    • Iaroslav Plutenko, Mikhail Papkov, Kaupo Palo, Leopold Parts, Dmytro Fishman
    Pages 147-155

  17. Self-prompting Large Vision Models for Few-Shot Medical Image Segmentation

    • Qi Wu, Yuyao Zhang, Marawan Elbatel
    Pages 156-167

  18. Back Matter

    Pages 169-170
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Other volumes

  1. Domain Adaptation and Representation Transfer

Editors and Affiliations

  • CONICET/Universidad Nacional del Litoral, Tübingen, Germany

    Lisa Koch

  • King’s College London, London, UK

    M. Jorge Cardoso

  • CONICET / Universidad Nacional del Litoral, Santa Fe, Argentina

    Enzo Ferrante

  • University of Oxford, Oxford, UK

    Konstantinos Kamnitsas

  • Imperial College London, London, UK

    Mobarakol Islam

  • Chinese University of Hong Kong, Hong Kong, Hong Kong

    Meirui Jiang

  • Nvidia GmbH, Munich, Germany

    Nicola Rieke

  • University of Edinburgh, Edinburgh, UK

    Sotirios A. Tsaftaris

  • Nvidia (United States), Santa Clara, USA

    Dong Yang

Bibliographic Information

  • Book Title: Domain Adaptation and Representation Transfer

  • Book Subtitle: 5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings

  • Editors: Lisa Koch, M. Jorge Cardoso, Enzo Ferrante, Konstantinos Kamnitsas, Mobarakol Islam, Meirui Jiang, Nicola Rieke, Sotirios A. Tsaftaris, … Dong Yang

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-45857-6

  • 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 2024

  • Softcover ISBN: 978-3-031-45856-9Published: 14 October 2023

  • eBook ISBN: 978-3-031-45857-6Published: 13 October 2023

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: X, 170

  • Number of Illustrations: 4 b/w illustrations, 40 illustrations in colour

  • Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Computer Applications, Machine Learning, Computing Milieux, Computer Applications

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