Accelerating whole-sample polarization-resolved second harmonic generation imaging in mammary gland tissue via generative adversarial networks
Bibliographic record
Abstract
Polarization second harmonic generation (P-SHG) imaging is a powerful technique for studying the structure and properties of biological and material samples. However, conventional whole-sample P-SHG imaging is time consuming and requires expensive equipment. This paper introduces a novel approach that significantly improves imaging resolution under conditions of reduced imaging time and resolution, utilizing enhanced super-resolution generative adversarial networks (ESRGAN) to upscale low-resolution images. We demonstrate that this innovative approach maintains high image quality and analytical accuracy, while reducing the imaging time by more than 95%. We also discuss the benefits of the proposed method for reducing laser-induced photodamage, lowering the cost of optical components, and increasing the accessibility and applicability of P-SHG imaging in various fields. Our work significantly advances whole-sample mammary gland P-SHG imaging and opens new possibilities for scientific discovery and innovation.
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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 | 0.000 |
| 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".