Predict Patient Self-reported Race from Skin Histological Images
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912510592942080 |
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| author | Chen, Shengjia Verma, Ruchika Clare, Kevin Jegminat, Jannes Alleva, Eugenia Huang, Kuan-lin Veremis, Brandon Fuchs, Thomas Campanella, Gabriele |
| author_facet | Chen, Shengjia Verma, Ruchika Clare, Kevin Jegminat, Jannes Alleva, Eugenia Huang, Kuan-lin Veremis, Brandon Fuchs, Thomas Campanella, Gabriele |
| contents | Artificial Intelligence (AI) has demonstrated success in computational pathology (CPath) for disease detection, biomarker classification, and prognosis prediction. However, its potential to learn unintended demographic biases, particularly those related to social determinants of health, remains understudied. This study investigates whether deep learning models can predict self-reported race from digitized dermatopathology slides and identifies potential morphological shortcuts. Using a multisite dataset with a racially diverse population, we apply an attention-based mechanism to uncover race-associated morphological features. After evaluating three dataset curation strategies to control for confounding factors, the final experiment showed that White and Black demographic groups retained high prediction performance (AUC: 0.799, 0.762), while overall performance dropped to 0.663. Attention analysis revealed the epidermis as a key predictive feature, with significant performance declines when these regions were removed. These findings highlight the need for careful data curation and bias mitigation to ensure equitable AI deployment in pathology. Code available at: https://github.com/sinai-computational-pathology/CPath_SAIF. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21912 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predict Patient Self-reported Race from Skin Histological Images Chen, Shengjia Verma, Ruchika Clare, Kevin Jegminat, Jannes Alleva, Eugenia Huang, Kuan-lin Veremis, Brandon Fuchs, Thomas Campanella, Gabriele Computer Vision and Pattern Recognition Computational Engineering, Finance, and Science Artificial Intelligence (AI) has demonstrated success in computational pathology (CPath) for disease detection, biomarker classification, and prognosis prediction. However, its potential to learn unintended demographic biases, particularly those related to social determinants of health, remains understudied. This study investigates whether deep learning models can predict self-reported race from digitized dermatopathology slides and identifies potential morphological shortcuts. Using a multisite dataset with a racially diverse population, we apply an attention-based mechanism to uncover race-associated morphological features. After evaluating three dataset curation strategies to control for confounding factors, the final experiment showed that White and Black demographic groups retained high prediction performance (AUC: 0.799, 0.762), while overall performance dropped to 0.663. Attention analysis revealed the epidermis as a key predictive feature, with significant performance declines when these regions were removed. These findings highlight the need for careful data curation and bias mitigation to ensure equitable AI deployment in pathology. Code available at: https://github.com/sinai-computational-pathology/CPath_SAIF. |
| title | Predict Patient Self-reported Race from Skin Histological Images |
| topic | Computer Vision and Pattern Recognition Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2507.21912 |