Author

Hermione Warr

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#artificial intelligence Preprint Sep 2026

Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables

Machine learning models can achieve strong test performance while relying on demographic or acquisition-related shortcuts. We propose counterfactual (CF) marginalisation as a test-time evaluation procedure for assessing robustness of classification models to such variables. Given a CF image generator, we intervene on n...

Yasin Ibrahim, Hermione Warr, R. Evans et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Error Detection for PET/CT Radiology Reports: Domain-Specific vs Large Language Models

It is suggested that domain-specific training matters more than model scale for PET/CT report error detection, supporting compact models as an accurate and computationally efficient approach to automated radiology report quality assurance.

Hermione Warr, Harry Anthony, Lilli J. Freischem et al. · 0 citations