Useful nonrobust features are ubiquitous in biomedical images
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911621496963072 |
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| author | Mouton, Coenraad Rabe, Randle Koser, Niklas C. Krekiehn, Nicolai Hansen, Christopher Hövener, Jan-Bernd Glüer, Claus-C. |
| author_facet | Mouton, Coenraad Rabe, Randle Koser, Niklas C. Krekiehn, Nicolai Hansen, Christopher Hövener, Jan-Bernd Glüer, Claus-C. |
| contents | 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 degrade out-of-distribution performance, revealing a practical robustness-accuracy trade-off in medical imaging classification tasks that should be tailored to the requirements of the deployment setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22579 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Useful nonrobust features are ubiquitous in biomedical images Mouton, Coenraad Rabe, Randle Koser, Niklas C. Krekiehn, Nicolai Hansen, Christopher Hövener, Jan-Bernd Glüer, Claus-C. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning 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 degrade out-of-distribution performance, revealing a practical robustness-accuracy trade-off in medical imaging classification tasks that should be tailored to the requirements of the deployment setting. |
| title | Useful nonrobust features are ubiquitous in biomedical images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2604.22579 |