22 September 2022

MICCAI Conference

Medical Applications with Disentanglements (MAD)

👋 Welcome to the MICCAI MAD Workshop 2022

Generative models are powerful deep learning methods that have demonstrated great potential for applications in the medical domain. Their performance has continuously improved over the last few years. They address several challenges that are due to the lack of sufficient publicly available medical training data, by providing approaches for synthetic dataset creation, unsupervised pre-training, transfer learning, or generation of missing image modalities. However, generative models often generalize poorly, e.g., to data from different domains and are criticized as black boxes due to a lack of interpretability and controllability. Some of these concerns can be handled by analyzing and disentangling the latent space representation of generative models, encouraging a comprehensive, human interpretable, and compressed representation of the data. The goal of this MICCAI workshop is to analyze the different proposed definitions of disentangled representations, review state-of-the-art methods to achieve disentanglement, evaluate and compare existing quality metrics, as well as to discuss new ideas and methods. By considering the mathematical background alongside applied methods, the workshop will combine theory and practice. Furthermore, these foundations will be discussed in the context of present and future medical applications. Results of the workshop and future directions of the disentanglement approach for medical applications are planned to be summarized in a workshop paper.

Overview Papers and Tutorial

Jana Fragemann, Lynton Ardizzone, Jan Egger, Jens Kleesiek (2022). Review of Disentanglement Approaches for Medical Applications--Towards Solving the Gordian Knot of Generative Models in Healthcare.

PDF

Xiao Liu, Pedro Sanchez, Spyridon Thermos, Alison Q. O’Neil, Sotirios A. Tsaftaris (2021). Learning Disentangled Representations in the Imaging Domain.

PDF

Sotirios A. Tsaftaris, Alison Q. O’Neil, Spyridon Thermos, Xiao Liu, Pedro Sanchez (2021). DREAM Tutorial.

Project Video

Call for Papers:

We are looking for papers including:

Disentanglement Definitions
Disentanglement Metrics
Analyzing existing Models or Metrics
Application of Disentanglement Methods onto Medical/Clinical Datasets
New Disentanglement Models
Mathematical Background/Theory

We plan to publish the conference results with Springer Proceedings, thus, please use the template you find here. We accept abstracts (1-2 pages), short paper (3-7 pages) and full paper (8+ pages). Submit you paper here. The review process will be double-blind, thus please anonymize your submissions.

If you intend to submit a paper, please send us a short e-mail.

The authors of the selected papers will be invited to be co-authors of an overview article, which will be submitted to a top-tier journal.

Submission Deadline: ~~25 June 22, 23:59 Pacific Time~~ 03 July 22, 23:59 Pacific Time

Acceptance/Revision/Rejection Notification: ~~16 July 22~~ ~~21 July 22~~ 27.07.22

Camera ready paper version: ~~30 July 2022~~ 05 August 22