Shortcut learning leads to sex bias in deep learning models for photoacoustic tomography Article Swipe
Marcel Knopp
,
Christoph J Bender
,
Niklas Holzwarth
,
Yi Li
,
Julius Kempf
,
Milenko Caranovic
,
Ferdinand Knieling
,
Werner Lang
,
Ulrich Rother
,
Alexander Seitel
,
Lena Maier‐Hein
,
Kris K. Dreher
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s11548-025-03370-9
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s11548-025-03370-9
Purpose Shortcut learning has been identified as a source of algorithmic unfairness in medical imaging artificial intelligence (AI), but its impact on photoacoustic tomography (PAT), particularly concerning sex bias, remains underexplored. This study investigates this issue using peripheral artery disease (PAD) diagnosis as a specific clinical application. Methods To examine the potential for sex bias due to shortcut learning in convolutional neural network (CNNs) and assess how such biases might affect diagnostic predictions, we created training and test datasets with varying PAD prevalence between sexes. Using these datasets, we explored (1) whether CNNs can classify the sex from imaging data, (2) how sex-specific prevalence shifts impact PAD diagnosis performance and underdiagnosis disparity between sexes, and (3) how similarly CNNs encode sex and PAD features. Results Our study with 147 individuals demonstrates that CNNs can classify the sex from calf muscle PAT images, achieving an AUROC of 0.75. For PAD diagnosis, models trained on data with imbalanced sex-specific disease prevalence experienced significant performance drops (up to 0.21 AUROC) when applied to balanced test sets. Additionally, greater imbalances in sex-specific prevalence within the training data exacerbated underdiagnosis disparities between sexes. Finally, we identify evidence of shortcut learning by demonstrating the effective reuse of learned feature representations between PAD diagnosis and sex classification tasks. Conclusion CNN-based models trained on PAT data may engage in shortcut learning by leveraging sex-related features, leading to biased and unreliable diagnostic predictions. Addressing demographic-specific prevalence imbalances and preventing shortcut learning is critical for developing models in the medical field that are both accurate and equitable across diverse patient populations.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11548-025-03370-9
- https://link.springer.com/content/pdf/10.1007/s11548-025-03370-9.pdf
- OA Status
- hybrid
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410234923
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https://openalex.org/W4410234923Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s11548-025-03370-9Digital Object Identifier
- Title
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Shortcut learning leads to sex bias in deep learning models for photoacoustic tomographyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-09Full publication date if available
- Authors
-
Marcel Knopp, Christoph J Bender, Niklas Holzwarth, Yi Li, Julius Kempf, Milenko Caranovic, Ferdinand Knieling, Werner Lang, Ulrich Rother, Alexander Seitel, Lena Maier‐Hein, Kris K. DreherList of authors in order
- Landing page
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https://doi.org/10.1007/s11548-025-03370-9Publisher landing page
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https://link.springer.com/content/pdf/10.1007/s11548-025-03370-9.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s11548-025-03370-9.pdfDirect OA link when available
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Artificial intelligence, Convolutional neural network, Machine learning, Deep learning, Feature (linguistics), Computer science, Medicine, Disease, Pathology, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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