Causal Representation Learning with Observational Grouping for CXR Classification

• URL: http://arxiv.org/abs/2506.20582v1
• Date: Wed, 25 Jun 2025 16:17:36 GMT
• Title: Causal Representation Learning with Observational Grouping for CXR Classification
• Authors: Rajat Rasal, Avinash Kori, Ben Glocker,
• Abstract summary: Identifiable causal representation learning seeks to uncover the true causal relationships underlying a data generation process.<n>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.
• Score: 17.11125452239702
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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