# Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization

arXiv: https://arxiv.org/abs/2306.09264 · HTML: https://mlsanity.com/paper/2306.09264/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 72 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 24 of 24)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [Multiaccuracy: Black-Box Post-Processing for Fairness in Classification](https://mlsanity.com/paper/1805.12317/) (2018)
- [Fair Regression: Quantitative Definitions and Reduction-based Algorithms](https://mlsanity.com/paper/1905.12843/) (2019)
- [Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations](https://mlsanity.com/paper/1707.00075/) (2017)
- [Fair Attribute Classification through Latent Space De-biasing](https://mlsanity.com/paper/2012.01469/) (2020)
- [Fairness by Learning Orthogonal Disentangled Representations](https://mlsanity.com/paper/2003.05707/) (2020)
- [Bias Mitigation Post-processing for Individual and Group Fairness](https://mlsanity.com/paper/1812.06135/) (2018)
- [Fair Contrastive Learning for Facial Attribute Classification](https://mlsanity.com/paper/2203.16209/) (2022)
- [Imbalanced Deep Learning by Minority Class Incremental Rectification](https://mlsanity.com/paper/1804.10851/) (2018)
- [Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers](https://mlsanity.com/paper/2203.04913/) (2022)
- [FR-Train: A Mutual Information-Based Approach to Fair and Robust Training](https://mlsanity.com/paper/2002.10234/) (2020)
- [Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models](https://mlsanity.com/paper/2203.01584/) (2022)
- [Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image](https://mlsanity.com/paper/1805.07549/) (2018)
- [Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing](https://mlsanity.com/paper/2007.13632/) (2020)

## Built on by (most cited first, 30 of 31)

- [FairCLIP: Harnessing Fairness in Vision-Language Learning](https://mlsanity.com/paper/2403.19949/) (2024)
- [FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models](https://mlsanity.com/paper/2407.00983/) (2024)
- [FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis](https://mlsanity.com/paper/2310.05055/) (2023)
- [FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification](https://mlsanity.com/paper/2407.08813/) (2024)
- [FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling](https://mlsanity.com/paper/2310.02492/) (2023)
- [LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models](https://mlsanity.com/paper/2410.01620/) (2024)
- [FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling](https://mlsanity.com/paper/2311.02189/) (2023)
- [Exposing and Mitigating Calibration Biases and Demographic Unfairness in MLLM Few-Shot In-Context Learning for Medical Image Classification](https://mlsanity.com/paper/2506.23298/) (2025)
- [Robust Fairness Vision-Language Learning for Medical Image Analysis](https://mlsanity.com/paper/2505.03153/) (2025)
- [Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insights](https://mlsanity.com/paper/2506.17337/) (2025)
- [X-GAN: A Generative AI-Powered Unsupervised Model for Main Vessel Segmentation of Glaucoma Screening](https://mlsanity.com/paper/2503.06743/) (2025)
- [Critical Appraisal of Fairness Metrics in Clinical Predictive AI](https://mlsanity.com/paper/2506.17035/) (2025)
- [See-in-Pairs: Reference Image-Guided Comparative Vision-Language Models for Medical Diagnosis](https://mlsanity.com/paper/2506.18140/) (2025)
- [LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology](https://mlsanity.com/paper/2509.25620/) (2025)
- [A Multi-Objective Evaluation Framework for Analyzing Utility-Fairness Trade-Offs in Machine Learning Systems](https://mlsanity.com/paper/2503.11120/) (2025)
- [FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA](https://mlsanity.com/paper/2508.00873/) (2025)
- [GlaBoost: A Multimodal Structured Framework for Glaucoma Risk Stratification](https://mlsanity.com/paper/2508.03750/) (2025)
- [BioVessel-Net and RetinaMix: Unsupervised Retinal Vessel Segmentation from OCTA Images](https://mlsanity.com/paper/2509.23617/) (2025)
- [Achieving Fairness Without Harm via Selective Demographic Experts](https://mlsanity.com/paper/2511.06293/) (2025)
- [Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review](https://mlsanity.com/paper/2411.05876/) (2024)
- [TransFair: Transferring Fairness from Ocular Disease Classification to Progression Prediction](https://mlsanity.com/paper/2412.00051/) (2024)
- [NeuroLIP: Interpretable and Fair Cross-Modal Alignment of fMRI and Phenotypic Text](https://mlsanity.com/paper/2503.21964/) (2025)
- [SWiFT: Soft-Mask Weight Fine-tuning for Bias Mitigation](https://mlsanity.com/paper/2508.18826/) (2025)
- [Toward Robust Medical Fairness: Debiased Dual-Modal Alignment via Text-Guided Attribute-Disentangled Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2508.18886/) (2025)
- [MultiFair: Multimodal Balanced Fairness-Aware Medical Classification with Dual-Level Gradient Modulation](https://mlsanity.com/paper/2510.07328/) (2025)
- [Fair-Eye Net: A Fair, Trustworthy, Multimodal Integrated Glaucoma Full Chain AI System](https://mlsanity.com/paper/2601.18464/) (2026)
- [GLEAM: A Multimodal Imaging Dataset and HAMM for Glaucoma Classification](https://mlsanity.com/paper/2603.12800/) (2026)
- [FairEnc: A Fair Vision-Language Model with Fair Vision and Text Encoders for Glaucoma Detection](https://mlsanity.com/paper/2605.04882/) (2026)
- [Worst-Group Equalized Odds Regularization for Multi-Attribute Fair Medical Image Classification](https://mlsanity.com/paper/2605.19214/) (2026)
- [Neuron Incidence Redistribution for Fairness in Medical Image Classification](https://mlsanity.com/paper/2605.19393/) (2026)

## Abstract (arXiv)

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 urlhttps://ophai.hms.harvard.edu/datasets/harvard-glaucoma-fairness-3300-samples/.