Multimodal system for optical biopsy of melanoma with integrated ultrasound, optical coherence tomography and Raman spectroscopy
Bibliographic record
Abstract
We introduce a new single-head multimodal optical system that integrates optical coherence tomography (OCT), 18 MHz ultrasound (US) tomography and Raman spectroscopy (RS), allowing for fast (<2 min) and noninvasive skin cancer diagnostics and lesion depth measurement. The OCT can deliver structural and depth information of smaller skin lesions (<1 mm), while the US allows to measure the penetration depth of thicker lesions (≥4 mm), and the RS analyzes the chemical composition from a small chosen spot (≤300 μm) that can be used to distinguish between benign and malignant melanoma. The RS and OCT utilize the same scanning and optical setup, allowing for co-localized measurements. The US on the other side is integrated with an acoustical reflector, which enables B-mode measurements on the same position as OCT and RS. The US B-mode scans can be translated across the sample by laterally moving the US transducer, which is made possible by the developed adapter with a flexible membrane. We present the results on custom-made liquid and agar phantoms that show the resolution and depth capabilities of the setup, as well as preliminary ex vivo measurements on mouse models with ∼4.3 mm thick melanoma.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".