Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation

arXiv:2605.06891 · cs.CV, cs.LG · Submitted 2026-05-07 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-05-07

Updated: 2026-09-26

Terminology

• Confident Learning — 17× in this paper
• Annotation Errors — 7× in this paper · explained in 1 episode
• Accuracy — 5× in this paper · explained in 1 episode
• Adaptation — 4× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Relative Risk — 4× in this paper
• Domain Shift — 3× in this paper · explained in 4 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Maximum Mean Discrepancy — 3× in this paper
• Regularization — 3× in this paper · explained in 3 episodes
• AUC — 2× in this paper · explained in 2 episodes
• AUROC — 2× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adversarial Debiasing — 2× in this paper · explained in 1 episode
• Algorithmic Bias — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Demographic Parity — 2× in this paper
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Equalized Odds — 2× in this paper
• Linear probe — 2× in this paper · explained in 2 episodes
• Segmentation Masks — 2× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adversarial Training — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• EMMA — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Global Average Pooling — 1× in this paper
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Individual Typology Angle — 1× in this paper
• Influence Functions — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Perplexity — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Detecting labeling bias using influence functions
• Estimating label quality and errors in semantic segmentation data via any model
• Mitigating Label Bias via Decoupled Confident Learning
• Investigating Label Bias and Representational Sources of Age-Related Disparities in Medical Segmentation
• Are demographically invariant models and representations in medical imaging fair?
• Common Limitations of Image Processing Metrics: A Picture Story
• That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation
• De-biased Representation Learning for Fairness with Unreliable Labels

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