Identifiable causal representation learning seeks to uncover the true causal relationships underlying a data generation process. In medical imaging, this presents opportunities to improve the generalisability and robustness of task-specific latent features. This work introduces the concept of grouping observations to learn identifiable representations for disease classification in chest X-rays via an end-to-end framework. Our experiments demonstrate that these causal representations improve generalisability and robustness across multiple classification tasks when grouping is used to enforce invariance w.r.t race, sex, and imaging views.
@article{arxiv.2506.20582,
title = {Causal Representation Learning with Observational Grouping for CXR Classification},
author = {Rajat Rasal and Avinash Kori and Ben Glocker},
journal= {arXiv preprint arXiv:2506.20582},
year = {2025}
}
Comments
Proceedings of the 3rd FAIMI Workshop at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2025, Daejeon, South Korea