Skin layer structure characterization via multi-contrast polarization-sensitive optical coherence tomography
Bibliographic record
Abstract
Skin cancer is one of the most prevalent types of cancer in the world, with a steadily increasing incidence rate and associated health burden [1, 2]. While it is generally treatable when detected, survival rates decrease dramatically as the disease progresses – highlighting the importance of early detection [3]. Unfortunately, the current gold standard in diagnosing skin cancers involves taking biopsies followed by histopathology, which is invasive and time consuming. Some studies have shown that the majority of biopsies ordered by primary care providers were found to be benign, meaning that biopsies are often performed when there is no cancer present [4]. Given this, there is a great interest in developing noninvasive diagnostic tools for skin imaging. Optical coherence tomography (OCT) is an imaging modality that is particularly well suited for this area, owing to its ability to provide high-resolution (3-15 μm) volumetric data at a penetration depth of up to 1.5 mm [5]. In a manner analogous to ultrasound, it provides cross-sectional images which can be comparable to histology slides [6]. However, conventional intensity-based OCT only provides structural information with no functional contrast, and as a result it has encountered difficulty in diagnosing specific cancers such as melanoma [7]. Polarization sensitive optical coherence tomography (PS-OCT) is a functional extension of OCT which can characterize polarization properties such as the birefringence of tissue samples – birefringence specifically occurs in fibrous structures such as collagen [8]. Several groups have investigated birefringence in skin tissue using PS-OCT, but little work has been done with the more recently defined degree of polarization uniformity (DOPU) contrast. The functional contrast provided by DOPU PS-OCT imaging can provide localized, depth-resolved information on the polarization scrambling properties of samples. A specific example of this is in ophthalmic imaging, where DOPU contrast in PS-OCT imaging demonstrated the ability to segment the retinal pigment epithelium – a layer otherwise hard to differentiate in intensity-based OCT imaging [9]. To our knowledge, very few if any groups have investigated PS-OCT imaging with DOPU contrast in understanding the layered-structure of skin. We have recently investigated the DOPU in skin tissue phantoms and reported that DOPU is sensitive to surface roughness – an important factor in differentiating skin cancers from benign lesions. Our group has a previously reported PS-OCT system that can simultaneously acquire reflectance, phase retardation (birefringence), and DOPU images that we aim to use in this study to expand on our previous work and further investigate polarization properties in skin in vivo [10].
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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) | 0.001 | 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 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".