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Deep Generative Models, and Data Augmentation, Labelling, and Imperfections

First Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Sandy Engelhardt

    1. Universitätsklinikum Heidelberg, Heidelberg, Germany

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  2. Ilkay Oksuz

    1. Istanbul Technical University, Istanbul, Turkey

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  3. Dajiang Zhu

    1. The University of Texas at Arlington, Arlington, USA

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  4. Yixuan Yuan

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

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  5. Anirban Mukhopadhyay

    1. TU Darmstadt, Darmstadt, Germany

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  6. Nicholas Heller

    1. University of Minnesota, Minneapolis, USA

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  7. Sharon Xiaolei Huang

    1. Pennsylvania State University, University Park, USA

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  8. Hien Nguyen

    1. University of Houston, Houston, USA

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  9. Raphael Sznitman

    1. University of Bern, Bern, Switzerland

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  10. Yuan Xue

    1. Johns Hopkins University, Baltimore, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13003)

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

Included in the following conference series:

Conference proceedings info: DALI 2021, DGM4MICCAI 2021.

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Table of contents (27 papers)

  1. Front Matter

    Pages i-xv
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  2. Image-to-Image Translation, Synthesis

    1. Front Matter

      Pages 1-1
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    2. Frequency-Supervised MR-to-CT Image Synthesis

      • Zenglin Shi, Pascal Mettes, Guoyan Zheng, Cees Snoek
      Pages 3-13

    3. Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domain

      • Alex Ling Yu Hung, John Galeotti
      Pages 14-23

    4. 3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images

      • Sungmin Hong, Razvan Marinescu, Adrian V. Dalca, Anna K. Bonkhoff, Martin Bretzner, Natalia S. Rost et al.
      Pages 24-34

    5. Bridging the Gap Between Paired and Unpaired Medical Image Translation

      • Pauliina Paavilainen, Saad Ullah Akram, Juho Kannala
      Pages 35-44

    6. Conditional Generation of Medical Images via Disentangled Adversarial Inference

      • Mohammad Havaei, Ximeng Mao, Yipping Wang, Qicheng Lao
      Pages 45-66

    7. CT-SGAN: Computed Tomography Synthesis GAN

      • Ahmad Pesaranghader, Yiping Wang, Mohammad Havaei
      Pages 67-79

  3. Applications and Evaluation

    1. Front Matter

      Pages 81-81
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    2. Hierarchical Probabilistic Ultrasound Image Inpainting via Variational Inference

      • Alex Ling Yu Hung, Zhiqing Sun, Wanwen Chen, John Galeotti
      Pages 83-92

    3. CaCL: Class-Aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patterns

      • Ruining Deng, Quan Liu, Shunxing Bao, Aadarsh Jha, Catie Chang, Bryan A. Millis et al.
      Pages 93-102

    4. BrainNetGAN: Data Augmentation of Brain Connectivity Using Generative Adversarial Network for Dementia Classification

      • Chao Li, Yiran Wei, Xi Chen, Carola-Bibiane Schönlieb
      Pages 103-111

    5. Evaluating GANs in Medical Imaging

      • Lorenzo Tronchin, Rosa Sicilia, Ermanno Cordelli, Sara Ramella, Paolo Soda
      Pages 112-121

  4. AdaptOR Challenge

    1. Front Matter

      Pages 123-123
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    2. Improved Heatmap-Based Landmark Detection

      • Huifeng Yao, Ziyu Guo, Yatao Zhang, Xiaomeng Li
      Pages 125-133

    3. Cross-Domain Landmarks Detection in Mitral Regurgitation

      • Jiacheng Wang, Haojie Wang, Ruochen Mu, Liansheng Wang
      Pages 134-141

  5. DALI 2021

    1. Front Matter

      Pages 143-143
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    2. Scalable Semi-supervised Landmark Localization for X-ray Images Using Few-Shot Deep Adaptive Graph

      • Xiao-Yun Zhou, Bolin Lai, Weijian Li, Yirui Wang, Kang Zheng, Fakai Wang et al.
      Pages 145-153

    3. Semi-supervised Surgical Tool Detection Based on Highly Confident Pseudo Labeling and Strong Augmentation Driven Consistency

      • Wenjing Jiang, Tong Xia, Zhiqiong Wang, Fucang Jia
      Pages 154-162

    4. One-Shot Learning for Landmarks Detection

      • Zihao Wang, Clair Vandersteen, Charles Raffaelli, Nicolas Guevara, François Patou, Hervé Delingette
      Pages 163-172
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  1. Deep Generative Models, and Data Augmentation, Labelling, and Imperfections

Keywords

About this book

This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021,  and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 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.

DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community.

For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorousstudy of medical data related to machine learning systems. 

 


Editors and Affiliations

  • Universitätsklinikum Heidelberg, Heidelberg, Germany

    Sandy Engelhardt

  • Istanbul Technical University, Istanbul, Turkey

    Ilkay Oksuz

  • The University of Texas at Arlington, Arlington, USA

    Dajiang Zhu

  • University of Hong Kong, Hong Kong, Hong Kong

    Yixuan Yuan

  • TU Darmstadt, Darmstadt, Germany

    Anirban Mukhopadhyay

  • University of Minnesota, Minneapolis, USA

    Nicholas Heller

  • Pennsylvania State University, University Park, USA

    Sharon Xiaolei Huang

  • University of Houston, Houston, USA

    Hien Nguyen

  • University of Bern, Bern, Switzerland

    Raphael Sznitman

  • Johns Hopkins University, Baltimore, USA

    Yuan Xue

Bibliographic Information

  • Book Title: Deep Generative Models, and Data Augmentation, Labelling, and Imperfections

  • Book Subtitle: First Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings

  • Editors: Sandy Engelhardt, Ilkay Oksuz, Dajiang Zhu, Yixuan Yuan, Anirban Mukhopadhyay, Nicholas Heller, Sharon Xiaolei Huang, Hien Nguyen, Raphael Sznitman, … Yuan Xue

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-88210-5

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2021

  • Softcover ISBN: 978-3-030-88209-9Published: 30 September 2021

  • eBook ISBN: 978-3-030-88210-5Published: 29 September 2021

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XV, 278

  • Number of Illustrations: 22 b/w illustrations, 82 illustrations in colour

  • Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Artificial Intelligence, Computational Biology/Bioinformatics

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