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Deep Generative Models

Third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Anirban Mukhopadhyay

    1. TU Darmstadt, Darmstadt, Germany

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  2. Ilkay Oksuz

    1. Istanbul Technical University, Istanbul, Türkiye

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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 14533)

Included in the following conference series:

Conference proceedings info: MICCAI 2023.

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Table of contents (23 papers)

  1. Front Matter

    Pages i-xi
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  2. Methods

    1. Front Matter

      Pages 1-1
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    2. Privacy Distillation: Reducing Re-identification Risk of Diffusion Models

      • Virginia Fernandez, Pedro Sanchez, Walter Hugo Lopez Pinaya, Grzegorz Jacenków, Sotirios A. Tsaftaris, M. Jorge Cardoso
      Pages 3-13

    3. Federated Multimodal and Multiresolution Graph Integration for Connectional Brain Template Learning

      • Jia Ji, Islem Rekik
      Pages 14-24

    4. Metrics to Quantify Global Consistency in Synthetic Medical Images

      • Daniel Scholz, Benedikt Wiestler, Daniel Rueckert, Martin J. Menten
      Pages 25-34

    5. MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images

      • Sergio Naval Marimont, Vasilis Siomos, Giacomo Tarroni
      Pages 35-44

    6. Towards Generalised Neural Implicit Representations for Image Registration

      • Veronika A. Zimmer, Kerstin Hammernik, Vasiliki Sideri-Lampretsa, Wenqi Huang, Anna Reithmeir, Daniel Rueckert et al.
      Pages 45-55

    7. Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis

      • Salman Ul Hassan Dar, Arman Ghanaat, Jannik Kahmann, Isabelle Ayx, Theano Papavassiliu, Stefan O. Schoenberg et al.
      Pages 56-65

    8. ViT-DAE: Transformer-Driven Diffusion Autoencoder for Histopathology Image Analysis

      • Xuan Xu, Saarthak Kapse, Rajarsi Gupta, Prateek Prasanna
      Pages 66-76

    9. Anomaly Guided Generalizable Super-Resolution of Chest X-Ray Images Using Multi-level Information Rendering

      • Vamshi Vardhan Yadagiri, Sekhar Reddy, Angshuman Paul
      Pages 77-85

    10. Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation

      • Yunguan Fu, Yiwen Li, Shaheer U. Saeed, Matthew J. Clarkson, Yipeng Hu
      Pages 86-95

  3. Applications

    1. Front Matter

      Pages 97-97
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    2. Diffusion-Based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images

      • Mohamed Akrout, Bálint Gyepesi, Péter Holló, Adrienn Poór, Blága Kincső, Stephen Solis et al.
      Pages 99-109

    3. Unsupervised Anomaly Detection in 3D Brain FDG PET: A Benchmark of 17 VAE-Based Approaches

      • Ravi Hassanaly, Camille Brianceau, Olivier Colliot, Ninon Burgos
      Pages 110-120

    4. Characterizing the Features of Mitotic Figures Using a Conditional Diffusion Probabilistic Model

      • Cagla Deniz Bahadir, Benjamin Liechty, David J. Pisapia, Mert R. Sabuncu
      Pages 121-131

    5. A 3D Generative Model of Pathological Multi-modal MR Images and Segmentations

      • Virginia Fernandez, Walter Hugo Lopez Pinaya, Pedro Borges, Mark S. Graham, Tom Vercauteren, M. Jorge Cardoso
      Pages 132-142

    6. Rethinking a Unified Generative Adversarial Model for MRI Modality Completion

      • Yixiao Yuan, Yawen Huang, Yi Zhou
      Pages 143-153

    7. Diffusion Models for Generative Histopathology

      • Niranjan Sridhar, Michael Elad, Carson McNeil, Ehud Rivlin, Daniel Freedman
      Pages 154-163

    8. Shape-Guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature

      • Yash Deo, Haoran Dou, Nishant Ravikumar, Alejandro F. Frangi, Toni Lassila
      Pages 164-173

    9. Pre-training with Diffusion Models for Dental Radiography Segmentation

      • Jérémy Rousseau, Christian Alaka, Emma Covili, Hippolyte Mayard, Laura Misrachi, Willy Au
      Pages 174-182
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Other volumes

  1. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  2. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  3. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  4. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  5. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  6. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  7. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  8. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  9. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  10. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  11. Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology

  12. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops

  13. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops

  14. Deep Generative Models

  15. Myopic Maculopathy Analysis

Keywords

About this book

This LNCS conference volume constitutes the proceedings of the third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 2023. The 23 full papers included in this volume were carefully reviewed and selected from 38 submissions.

The conference presents topics ranging from methodology, causal inference, latent interpretation, generative factor analysis to applications such as mammography, vessel imaging, and surgical

Videos.

Editors and Affiliations

  • TU Darmstadt, Darmstadt, Germany

    Anirban Mukhopadhyay

  • Istanbul Technical University, Istanbul, Türkiye

    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: Third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

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

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-53767-7

  • 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: 978-3-031-53766-0Published: 20 February 2024

  • eBook ISBN: 978-3-031-53767-7Published: 19 February 2024

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 248

  • Number of Illustrations: 10 b/w illustrations, 76 illustrations in colour

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

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