mlsanityData to 7 Oct 2026

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

arXiv 2306.09264 ↗First posted 15 Jun 2023cs.CVcited by 31

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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/.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 24 of its 72 references that are in the map. First posted on arXiv 15 Jun 2023.

Builds on

24 of 72 references.

References · 24

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  3. 2020Supervised Contrastive Learningcited by 2,803
  4. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
  5. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  6. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  7. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,239
  8. 2019Learning Imbalanced Datasets with Label-Distribution-Aware Margin Losscited by 987
  9. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  10. 2018A Reductions Approach to Fair Classificationcited by 585
  11. 2017Age Progression/Regression by Conditional Adversarial Autoencodercited by 426
  12. 2018Multiaccuracy: Black-Box Post-Processing for Fairness in Classificationcited by 221
12 more
  1. 2019Fair Regression: Quantitative Definitions and Reduction-based Algorithmscited by 168
  2. 2017Data Decisions and Theoretical Implications when Adversarially Learning Fair Representationscited by 165
  3. 2020Fair Attribute Classification through Latent Space De-biasingcited by 125
  4. 2020Fairness by Learning Orthogonal Disentangled Representationscited by 76
  5. 2018Bias Mitigation Post-processing for Individual and Group Fairnesscited by 72
  6. 2022Fair Contrastive Learning for Facial Attribute Classificationcited by 71
  7. 2018Imbalanced Deep Learning by Minority Class Incremental Rectificationcited by 65
  8. 2022Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifierscited by 53
  9. 2020FR-Train: A Mutual Information-Based Approach to Fair and Robust Trainingcited by 52
  10. 2022Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Modelscited by 47
  11. 2018Disc-aware Ensemble Network for Glaucoma Screening from Fundus Imagecited by 39
  12. 2020Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasingcited by 32

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 16; 2026: 6.

31 papers.

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  1. 2024FairCLIP: Harnessing Fairness in Vision-Language Learningcited by 69
  2. 2024FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Modelscited by 47
  3. 2023FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysiscited by 23
  4. 2024FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classificationcited by 19
  5. 2023FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scalingcited by 13
  6. 2024LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Modelscited by 7
  7. 2025Exposing and Mitigating Calibration Biases and Demographic Unfairness in MLLM Few-Shot In-Context Learning for Medical Image Classificationcited by 5
  8. 2023FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scalingcited by 4
  9. 2025Robust Fairness Vision-Language Learning for Medical Image Analysiscited by 4
  10. 2025Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insightscited by 3
  11. 2025X-GAN: A Generative AI-Powered Unsupervised Model for Main Vessel Segmentation of Glaucoma Screeningcited by 2
  12. 2025Critical Appraisal of Fairness Metrics in Clinical Predictive AIcited by 2
19 more
  1. 2025See-in-Pairs: Reference Image-Guided Comparative Vision-Language Models for Medical Diagnosiscited by 2
  2. 2025A Multi-Objective Evaluation Framework for Analyzing Utility-Fairness Trade-Offs in Machine Learning Systemscited by 1
  3. 2025FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRAcited by 1
  4. 2025GlaBoost: A Multimodal Structured Framework for Glaucoma Risk Stratificationcited by 1
  5. 2025BioVessel-Net and RetinaMix: Unsupervised Retinal Vessel Segmentation from OCTA Imagescited by 1
  6. 2025LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmologycited by 1
  7. 2025Achieving Fairness Without Harm via Selective Demographic Expertscited by 1
  8. 2024Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Reviewcited by 0
  9. 2024TransFair: Transferring Fairness from Ocular Disease Classification to Progression Predictioncited by 0
  10. 2025NeuroLIP: Interpretable and Fair Cross-Modal Alignment of fMRI and Phenotypic Textcited by 0
  11. 2025SWiFT: Soft-Mask Weight Fine-tuning for Bias Mitigationcited by 0
  12. 2025Toward Robust Medical Fairness: Debiased Dual-Modal Alignment via Text-Guided Attribute-Disentangled Prompt Learning for Vision-Language Modelscited by 0
  13. 2025MultiFair: Multimodal Balanced Fairness-Aware Medical Classification with Dual-Level Gradient Modulationcited by 0
  14. 2026Fair-Eye Net: A Fair, Trustworthy, Multimodal Integrated Glaucoma Full Chain AI Systemcited by 0
  15. 2026GLEAM: A Multimodal Imaging Dataset and HAMM for Glaucoma Classificationcited by 0
  16. 2026FairEnc: A Fair Vision-Language Model with Fair Vision and Text Encoders for Glaucoma Detectioncited by 0
  17. 2026Worst-Group Equalized Odds Regularization for Multi-Attribute Fair Medical Image Classificationcited by 0
  18. 2026Neuron Incidence Redistribution for Fairness in Medical Image Classificationcited by 0
  19. 2026Balanced Soft mixture-of-expert model for Glaucoma Detectioncited by 0