Simple, Safe, and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching
Hardware shifts, color variations, and changing patient characteristics between development and deployment routinely break trained medical image classifiers. Existing remedies fall short: standard color jittering provides insufficient diversity, while deep generative style transfer algorithms hallucinate features, destroy clinically relevant structures, and waste massive compute resources. To address this, we revisit classical statistical color matching and repurpose it as Colorist, a highly efficient data augmentation strategy that applies global mean-standard deviation matching directly in t
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 47%Multi-modal deep learning model for visual acuity prediction from wide field colour fundus imaging - Nature →
- LinkedLinked via arxiv author · 85%Sebastian Doerrich →
“Simple, Safe, and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching”
- LinkedLinked via arxiv author · 85%Francesco Di Salvo →
“Simple, Safe, and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching”
- LinkedLinked via arxiv author · 85%Shyam Nandan Rai →
“Simple, Safe, and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching”
- LinkedLinked via arxiv author · 85%Marco Lents →
“Simple, Safe, and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching”
- LinkedLinked via arxiv author · 85%Christian Ledig →
“Simple, Safe, and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching”