Causal Representation Learning with Observational Grouping for CXR Classification

Abstract

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.

Proceedings of the 3rd FAIMI Workshop at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2025, Daejeon, South Korea