Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization
Fairness (also known as equity interchangeably) in machine learning is
important for societal well-being, but limited public datasets hinder its
progress. Currently, no dedicated public medical datasets with imaging data for
fairness learning are available, though minority groups suffer from more health
issues. To address this gap, we introduce Harvard Glaucoma Fairness
(Harvard-GF), a retinal nerve disease dataset with both 2D and 3D imaging data
and balanced racial groups for glaucoma detection. Glaucoma is the leading
cause of irreversible blindness globally with Blacks having doubled glaucoma
prevalence than other races. We also propose a fair identity normalization
(FIN) approach to equalize the feature importance between different identity
groups. Our FIN approach is compared with various the-state-of-the-art fairness
learning methods with superior performance in the racial, gender, and ethnicity
fairness tasks with 2D and 3D imaging data, which demonstrate the utilities of
our dataset Harvard-GF for fairness learning. To facilitate fairness
comparisons between different models, we propose an equity-scaled performance
measure, which can be flexibly used to compare all kinds of performance metrics
in the context of fairness. The dataset and code are publicly accessible via
\url{https://ophai.hms.harvard.edu/datasets/harvard-glaucoma-fairness-3300-samples/}.