Deep Generative Models workshop @ MICCAI 2023


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. These models combine the advanced deep neural networks with classical density estimation (either explicit or implicit) for achieving state-of-the-art results. DGM4MICCAI workshop at MICCAI 2023 will be all about Deep Generative Models in Medical Image Computing and Computer Assisted Interventions.

Submission Details

The online submission for DGM4MICCAI is open until PLACEHOLDER. Contributions must be submitted online through the CMT submission system.

We seek contributions that include, but are not limited to:

  • Novel architectures, loss functions, and theoretical developments for:
    • GANs and Adversarial Learning
    • Variational Auto-Encoder
    • Disentanglement
    • Flow
    • Autoregressive models
  • Multi-Modality and Cross-Modality linking
  • Novel metrics and uncertainty estimates for performance assessment and interpretability of generative models
  • Generative models under limited, sparse and noisy image inputs
  • Supervised and Unsupervised Domain Adaptation, Transfer Learning and Multi-Task Learning
  • Segmentation, Detection, Synthesis, Reconstruction, Denoising, Supersampling, Registration
  • Image-to-Image translation for Synthetic Training Data Generation or Augmented Reality
  • Neural Rendering
  • Diffusion Models, Normalizing Flow Models, Invertible Networks

We particularly welcome papers driven by the theme "MIC meets CAI". Interesting novel applications of deep generative models in MIC and CAI beyond these topics are also welcome.

Workshop proceedings are published as part of Springer Nature's Lecture Notes in Computer Science (LNCS) series. Manuscripts will be reviewed in double-blinded peer-review. Please prepare your workshop papers according to the MICCAI submission guidelines (LNCS template, 8 pages maximum).
Supplementary material: PDF documents or mp4 videos, 10 MB maximum file size.


Event Date
Paper Submission Deadline TBD
Review Release TBD
Final Decision TBD
Camera ready papers due TBD

Preliminary Program

DGM4MICCAI workshop will be a half-day event, following the agenda below:
Start End Topic

Please note that times might be subject to change, so keep an eye on the schedule or follow us on Twitter to be up-to-date.

Keynote Speaker

Ke Li

Assistant Professor at Simon Fraser University



Oral Sessions

Organizing Committee

  • Sandy Engelhardt, Heidelberg University, Germany
  • Ilkay Oksuz, Istanbul Technical University, Turkey
  • Dajiang Zhu, University of Texas, USA
  • Yixuan Yuan, City University of Hong Kong, China
  • Anirban Mukhopadhyay, Technische Universität Darmstadt, Germany
Student organizers:
  • Lalith Sharan, University Hospital Heidelberg, Germany
  • Henry Krumb, TU Darmstadt, Germany
  • Moritz Fuchs, TU Darmstadt, Germany
  • Amin Ranem, TU Darmstadt, Germany
  • Caner Özer, Istanbul Technical University, Turkey
  • Chen Zhen, City University of Hong Kong
  • Guo Xiaoqing, City University of Hong Kong

Program Committee

  • Li Wang, University of Texas at Arlington, USA
  • Tong Zhang , Peng Cheng Laboratory, Shenzhen, China
  • Ping Lu, Oxford University, UK
  • Roxane Licandro, Medical University of Vienna, Austria
  • Chen Qin, University of Edinburgh, UK
  • Veronika Zimmer, TU Muenchen, Germany
  • Dwarikanath Mahapatra, Inception Institute of AI, UAE
  • Michael Sdika, CREATIS Lyon, France
  • Jelmer Wolterink, Univ. of Twente, The Netherlands
  • Alejandro Granados, King's College London, UK
  • Jinglei Lv, The University of Sydney, Australia
  • Shiba Kuanar, Mayo Clinic, USA
  • Onat Dalmaz, Bilkent University, Turkey
  • Yuri Tolkach, University Hospital Cologne, Germany