Learning disentangled representations in the imaging domain

Visual summary of disentangled representation learning applications in medical imaging. Red connections indicate vector-based disentanglement, while blue connections indicate tensor/vector-based one (CSD). The visual examples are taken from the papers and repositories of the applications that are reported in the Tables below.

Applications of Disentanglement

Exemplar methods that exploit disentanglement to improve challenging tasks in computer vision and medical image analysis.

NOTE: The scope of this tutorial is not to survey every existing application for the to domains. If you feel that we should include your research as part of this tutorial we would be happy to do so. Please send an email to any of the contributor and help us create a place where everyone can understand and leverage disentanglement!

Computer Vision

For each application we denote the type of disentanglement as either "Vector" or content-style "C-S".

Medical Image Analysis

For each application we denote the type of disentanglement as either "Vector" or content-style "C-S".

Note that the SDNet model has also been implemented in PyTorch and has been integrated into the GaNDLF framework.

Metrics

Measuring disentanglement in single vector latent variables:

Measuring disentanglement between two latent variables of the same or different dimensionality:

Citation

@article{liu2022learning,
 title={Learning disentangled representations in the imaging domain},
 author={Liu, Xiao and Sanchez, Pedro and Thermos, Spyridon and O’Neil, Alison Q and Tsaftaris, Sotirios A},
 journal={Medical Image Analysis},
 pages={102516},
 year={2022},
 publisher={Elsevier}
}