Signal-aware synthesis of tissue polarization uniformity from OCT images guided by an SNR-based heuristic
Bibliographic record
Abstract
Polarization-sensitive optical coherence tomography (PS-OCT) is a powerful imaging modality that captures both structural and polarization-related tissue features, offering significant diagnostic value. Among these, the degree of polarization uniformity (DOPU) is critical for characterizing tissue microstructure. However, obtaining DOPU images typically requires specialized hardware and complex system configurations. To address this limitation, we propose a knowledge-guided deep generative framework, signal attention GAN (SA-GAN), to synthesize DOPU images directly from standard OCT intensity scans. SA-GAN integrates a signal-guided attention mechanism inspired by signal-to-noise ratio (SNR) principles, enabling selective focus on regions with meaningful polarization patterns while suppressing noise-dominated areas. This design allows for the generation of accurate, high-fidelity DOPU images without additional imaging hardware. We validated SA-GAN on three independent datasets: SKIN-PSOCT, CARTILAGE-PSOCT, and the public Retinal-OCT2017 dataset. On SKIN-PSOCT, SA-GAN achieved a structural similarity index measure (SSIM) of 97.8% and a peak signal-to-noise ratio (PSNR) of 28.6 dB. On CARTILAGE-PSOCT, it reached an SSIM of 93.9% and a PSNR of 24.8 dB in cross-dataset testing. Applied to the Retinal-OCT2017 dataset, SA-GAN achieved state-of-the-art performance in a four-class retinal disease classification task. These results demonstrate the robustness and generalizability of our method. SA-GAN provides a cost-effective and practical solution to extend PS-OCT capabilities, supporting the development of intelligent imaging systems for biomedical diagnostics and digital medicine. Our code is available via this link: https://github.com/Yuhengw/SA-GAN .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".