FairDD uses domain-incremental replay, mixup, contrastive learning, and distillation to improve the accuracy-fairness tradeoff of dermatological classifiers on Fitzpatrick-17k and ISIC 2019.
A review of deep learning in medical imaging: Imaging traits, technol- ogy trends, case studies with progress highlights, and future promises,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis
FairDD uses domain-incremental replay, mixup, contrastive learning, and distillation to improve the accuracy-fairness tradeoff of dermatological classifiers on Fitzpatrick-17k and ISIC 2019.