Tissue Imaging Technique Using Near-Infrared Illumination of Whispering Gallery Mode Silicon-Based Resonator
Bibliographic record
Abstract
This letter introduces a novel technique for achieving high-precision 2-D tissue imaging by exploiting the sensitivity of a whispering gallery mode (WGM) silicon resonator’s conductivity to near-infrared (NIR) illumination. The WGM silicon resonator, in conjunction with a microstrip line, acts as the primary sensing element. To ensure precise imaging, the tissue under test (TUT) specimen is meticulously positioned on the resonator at a specific distance and manipulated using a 2-D scanner with 3-mm steps. By directing NIR light emitted from a light-emitting diode (LED) through the scanning TUT sample onto the WGM resonator, variations in the silicon resonator’s conductivity are harnessed, resulting in changes in the magnitude of the transmission coefficient ($S_{21}$). The alteration in$S_{21}$during scanning is contingent upon the absorption of NIR through TUT. As the TUT undergoes scanning, the measured transmission coefficient$S_{21}$parameters are transformed into a 2-D image map. This method effectively discriminates between fat and muscle tissues, underscoring the feasibility and practicality of this approach. Importantly, the proposed methodology shows promise for detecting various biosensors and holds potential applications in breast cancer detection.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".