One-shot Stokes polarimetry for low-cost skin cancer detection
Bibliographic record
Abstract
Introduction: Management of skin cancer worldwide is often a challenge of scale, in that the resources available to detect and treat skin cancer are outweighed by the number of potential cases presented. This project aims to develop oneshot Stokes polarimetry using low-cost components to create a widely available skin cancer detection tool. Methods: A probe was developed to perform one-shot Stokes polarimetry on skin lesions in-vivo. Stokes polarimetry is an optical technique in which a laser of known polarization is fired at a target, and the altered polarization state of the returning light is measured. Typically, measuring a polarization state requires sequential measurements with four polarizing filters, however this probe contains four separate detectors to take these measurements in one shot. This probe was designed to perform at a lower cost and higher speed than traditional polarization methods. The Stokes vector is assessed as opposed to a Mueller matrix image to reduce the number of optical components and measurements required. The probe uses photodiodes and non-actuating film polarizing filters as detectors, and a partially-coherent laser diode as its illumination source. Results: Validation tests of each probe component, and the complete system put together, were performed to confirm the probe’s performance despite its low-cost components. This probe’s potential is demonstrated in a pilot clinical study on 69 skin lesions. The degree of polarization was found to be a factor by which melanoma could be potentially separated from other types of skin lesions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".