One-shot Stokes polarimetry for low-cost skin cancer detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it