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Deep Generative Models

Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Anirban Mukhopadhyay

    1. TU Darmstadt, Darmstadt, Germany

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  2. Ilkay Oksuz

    1. Istanbul Technical University, Istanbul, Turkey

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  3. Sandy Engelhardt

    1. University Hospital Heidelberg, Heidelberg, Germany

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  4. Dajiang Zhu

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

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  5. Yixuan Yuan

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

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13609)

Included in the following conference series:

Conference proceedings info: DGM4MICCAI 2022.

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About this book

This book constitutes the refereed proceedings of the Second MICCAI Workshop on Deep Generative Models, DG4MICCAI 2022, held in conjunction with MICCAI 2022, in September 2022. The workshops took place in Singapore. 

DG4MICCAI 2022 accepted 12 papers from the 15 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.

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Keywords

Table of contents (12 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Methods

    1. Front Matter

      Pages 1-1
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    2. Flow-Based Visual Quality Enhancer for Super-Resolution Magnetic Resonance Spectroscopic Imaging

      • Siyuan Dong, Gilbert Hangel, Eric Z. Chen, Shanhui Sun, Wolfgang Bogner, Georg Widhalm et al.
      Pages 3-13

    3. Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection

      • Ashay Patel, Petru-Daniel Tudosiu, Walter Hugo Lopez Pinaya, Gary Cook, Vicky Goh, Sebastien Ourselin et al.
      Pages 14-23

    4. Interpreting Latent Spaces of Generative Models for Medical Images Using Unsupervised Methods

      • Julian Schön, Raghavendra Selvan, Jens Petersen
      Pages 24-33

    5. What is Healthy? Generative Counterfactual Diffusion for Lesion Localization

      • Pedro Sanchez, Antanas Kascenas, Xiao Liu, Alison Q. O’Neil, Sotirios A. Tsaftaris
      Pages 34-44

    6. Learning Generative Factors of EEG Data with Variational Auto-Encoders

      • Maksim Zhdanov, Saskia Steinmann, Nico Hoffmann
      Pages 45-54

    7. An Image Feature Mapping Model for Continuous Longitudinal Data Completion and Generation of Synthetic Patient Trajectories

      • Clément Chadebec, Evi M. C. Huijben, Josien P. W. Pluim, Stéphanie Allassonnière, Maureen A. J. M. van Eijnatten
      Pages 55-64

  3. Applications

    1. Front Matter

      Pages 65-65
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    2. Novel View Synthesis for Surgical Recording

      • Mana Masuda, Hideo Saito, Yoshifumi Takatsume, Hiroki Kajita
      Pages 67-76

    3. Anomaly Detection Using Generative Models and Sum-Product Networks in Mammography Scans

      • Marc Dietrichstein, David Major, Martin Trapp, Maria Wimmer, Dimitrios Lenis, Philip Winter et al.
      Pages 77-86

    4. Image Translation Based Nuclei Segmentation for Immunohistochemistry Images

      • Roger Trullo, Quoc-Anh Bui, Qi Tang, Reza Olfati-Saber
      Pages 87-96

    5. 3D (c)GAN for Whole Body MR Synthesis

      • Daniel Mensing, Jochen Hirsch, Markus Wenzel, Matthias Günther
      Pages 97-105

    6. Airway Measurement by Refinement of Synthetic Images Improves Mortality Prediction in Idiopathic Pulmonary Fibrosis

      • Ashkan Pakzad, Mou-Cheng Xu, Wing Keung Cheung, Marie Vermant, Tinne Goos, Laurens J. De Sadeleer et al.
      Pages 106-116

    7. Brain Imaging Generation with Latent Diffusion Models

      • Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F. Da Costa, Virginia Fernandez, Parashkev Nachev et al.
      Pages 117-126

  4. Back Matter

    Pages 127-127
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Other volumes

  1. Deep Generative Models

Editors and Affiliations

  • TU Darmstadt, Darmstadt, Germany

    Anirban Mukhopadhyay

  • Istanbul Technical University, Istanbul, Turkey

    Ilkay Oksuz

  • University Hospital Heidelberg, Heidelberg, Germany

    Sandy Engelhardt

  • The University of Texas at Arlington, Arlington, USA

    Dajiang Zhu

  • University of Hong Kong, Hong Kong, Hong Kong

    Yixuan Yuan

Bibliographic Information

  • Book Title: Deep Generative Models

  • Book Subtitle: Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

  • Editors: Anirban Mukhopadhyay, Ilkay Oksuz, Sandy Engelhardt, Dajiang Zhu, Yixuan Yuan

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-18576-2

  • 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: 978-3-031-18575-5Published: 08 October 2022

  • eBook ISBN: 978-3-031-18576-2Published: 07 October 2022

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: X, 127

  • Number of Illustrations: 8 b/w illustrations, 36 illustrations in colour

  • Topics: Image Processing and Computer Vision, Machine Learning, Computers and Education, Computer Applications

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