An adaptation of Confident Learning detects directional label errors in segmentation datasets without clean ground truth and leverages encoder feature separability to mitigate bias and equalize performance across subgroups.
Exploring the interplay of label bias with subgroup size and separability: A case study in mammographic density
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation
An adaptation of Confident Learning detects directional label errors in segmentation datasets without clean ground truth and leverages encoder feature separability to mitigate bias and equalize performance across subgroups.