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 2024 will be all about Deep Generative Models in Medical Image Computing and Computer Assisted Interventions.

Submission Details

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
• Stable Diffusion Models
• Causal generative 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.

Program

Long Oral

Short Oral