We seek contributions that include, but are not limited to:
- Novel architectures, loss functions, and theoretical developments for Generative Adversarial Networks, 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
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.