A novel H and E color augmentation for domain invariance classification of unannotated histopathology prostate cancer images
Bibliographic record
Abstract
Most current deep learning models for hematoxylin and eosin (H&E) histopathology image analysis lack the power of generalization to datasets collected from other institutes due to the domain shift in the data. In this research, we study the domain shift problem on two prostate cancer (PCa) datasets collected from the Vancouver Prostate Centre (source dataset) and the University of Colorado (target dataset) and develop a novel center-based H&E color augmentation for cross-center model generalization. While previous work used methods such as random augmentation, color normalization, or learning domain-independent features to improve the robustness of the model to changes in H&E stains, our method first augments the H&E color space of the source dataset to color space of both datasets and then adds random color augmentation. Our method covers the larger range of the color distribution of both institutions resulting in a better generalization. We compared our method with two different State-Of-The-Art (SOTA) un-annotated domain adaptation methods: color normalization and unsupervised domain adversarial neural network (DANN) training, with an ablation study. Our proposed method improves the model performance on both the source and target datasets, and has the best performance on the unlabeled target dataset, showing promise as an approach to learning more generalizable features for histopathology image analysis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".