Useful nonrobust features are ubiquitous in biomedical images
We study whether deep networks for medical imaging learn useful nonrobust features - predictive input patterns that are not human interpretable and highly susceptible to small adversarial perturbations - and how these features impact test performance. We show that models trained only on nonrobust features achieve well above chance accuracy across five MedMNIST classification tasks, confirming their predictive value in-distribution. Conversely, adversarially trained models that primarily rely on robust features sacrifice in-distribution accuracy but yield markedly better performance under controlled distribution shifts (MedMNIST-C). Overall, nonrobust features boost standard accuracy yet d...
| Authors | Coenraad Mouton, Randle Rabe, Niklas C. Koser et al. |
|---|---|
| Primary category | eess.IV |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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