Self-supervised stain normalization empowers privacy-preserving and model generalization in digital pathology
Bibliographic record
Abstract
Digital pathology images from different hospitals often exhibit variations in color styles due to differences in staining processes, tissue handling, and scanning devices. Integrating data from multiple centers is essential for developing artificial intelligence-driven digital pathology (AIDP) models with improved generalization. However, privacy concerns complicate data sharing, hindering this integration. Here, we propose a self-supervised model, stain lookup table (StainLUT), that leverages the inherent structural similarity between pathology tissue samples of the same disease type across different medical centers and enables stain normalization without the need for cross-center data transfer. Applied to single-center AIDP models, we achieve cross-center tumor localization at the whole-slide level and tumor classification at the patch level, performing comparably to AIDP models trained on centralized or same-center data. StainLUT offers a privacy-preserving solution for stain normalization in unseen medical centers, and holds the potential to facilitate the future deployment of AIDP foundational models under privacy regulations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".