Eye Fairness: A Large-Scale 3D Imaging Dataset for Equitable Eye Diseases Screening and Fair Identity Scaling
Primary Area: datasets and benchmarks
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Keywords: Equitable Deep Learning, Fairness, Fair Loss Scaling, Healthcare Disparity
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TL;DR: Due to the lack of large-scale public datasets with 3D imaging data for fairness learning, we introduce the EyeFairness dataset covering major eye diseases and demographic attributes.
Abstract: Fairness or equity in machine learning is profoundly important for societal well-being, but limited public datasets hinder its progress, especially in the area of medicine. It is undeniable that fairness in medicine is one of the most important areas for fairness learning's applications. Currently, no large-scale public medical datasets with 3D imaging data for fairness learning are available, while 3D imaging data in modern clinics are standard tests for disease diagnosis. In addition, existing medical fairness datasets are actually repurposed datasets, and therefore they typically have limited demographic identity attributes with at most three identity attributes of age, gender and race for fairness modeling. To address this gap, we introduce our Eye Fairness dataset with 30,000 subjects (EyeFairness-30k) covering three major eye diseases including age-related macular degeneration, diabetic retinopathy and glaucoma affecting 380 million patients globally. Our EyeFairness dataset include both 2D fundus photos and 3D optical coherence tomography scans with six demographic identity attributes including age, gender, race, ethnicity, preferred language, and marital status. We also propose a fair identity scaling (FIS) approach combining group and individual scaling together to improve model fairness. Our FIS 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 EyeFairness dataset for fairness learning. To facilitate fairness comparisons between different models, we propose performance-scaled disparity measures, which can be to compare model fairness account for overall performance levels. The dataset and code are publicly accessible via https://github.com/anonymous4science/EyeFairness.
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Submission Number: 7116
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