Hybrid Supervised-Unsupervised CycleGAN for Virtual HER2 Immunohistochemistry from Hematoxylin and Eosin Stains
Bibliographic record
Abstract
HER2-status is a vital biomarker for breast cancer diagnosis and treatment, typically assessed using immunohistochemistry (IHC), a technique that is expensive and demanding of laboratory experience. Hematoxylin and eosin (H&E) staining, in contrast, is widely available and inexpensive, motivating approaches that can computationally translate H&E images into IHC. While previous work has explored translating H&E stains of breast tissue into IHC using purely unsupervised methods, this study introduces a hybrid CycleGAN framework that combines unsupervised cycle-consistency with supervised paired reconstruction objectives. By leveraging the paired structure of the BCI dataset, this approach significantly improves quantitative metrics (PSNR: 16.203 → 17.807 (Adam); SSIM: 0.373 → 0.4061 (AdamW) and visual fidelity compared to unsupervised-only baselines, narrowing the performance gap with supervised-only architectures while maintaining CycleGAN’s flexibility. These f indings show that incorporating limited supervision into cycle-consistent adversarial training enhances H&E-to-IHC translation quality, offering a more affordable and accessible pathway to HER2 screening.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".