Demographic bias in misdiagnosis by computational pathology models

Using publicly available data from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, as well as internal patient data, we show that whole-slide image classification models display marked performance disparities across different demographic groups when used to subtype breast and lung carcinomas and to predictmutations in gliomas. For example, when using common modeling approaches, we observed performance gaps between white and Black patients of 3.0% for breast cancer subtyping, 10.

Landry, L. G., Ali, N., Williams, D. R., Rehm, H. L. & Bonham, V. L. Lack of diversity in genomic databases is a barrier to translating precision medicine research into practice.Khor, S. et al. Racial and ethnic bias in risk prediction models for colorectal cancer recurrence when race and ethnicity are omitted as predictors.Dietze, E. C., Sistrunk, C., Miranda-Carboni, G., O’Reagan, R. & Seewaldt, V. L. Triple-negative breast cancer in African-American women: disparities versus biology.

Yao, J., Zhu, X., Jonnagaddala, J., Hawkins, N. & Huang, J. Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks.Google ScholarTellez, D. et al. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.Glocker, B., Jones, C., Bernhardt, M. & Winzeck, S. Algorithmic encoding of protected characteristics in chest X-ray disease detection models.

Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.Advances in Neural Information Processing SystemsThis work was supported in part by the Brigham and Women’s Hospital President’s Fund, BWH and Massachusetts General Hospital Pathology, and National Institute of General Medical Sciences R35GM138216 . R.J.C. was supported by the National Science Foundation Graduate Fellowship. Y.Y. was supported by the Takeda Fellowship. M.

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