Causal Representation Learning with Observational Grouping for CXR Classification
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909912913674240 |
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| author | Rasal, Rajat Kori, Avinash Glocker, Ben |
| author_facet | Rasal, Rajat Kori, Avinash Glocker, Ben |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_20582 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal Representation Learning with Observational Grouping for CXR Classification Rasal, Rajat Kori, Avinash Glocker, Ben Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | Causal Representation Learning with Observational Grouping for CXR Classification |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.20582 |