Near-Infrared Handheld Probe and Imaging System for Breast Tumor Localization
Bibliographic record
Abstract
Diffuse optical tomography (DOT) is a breast imaging modality that measures the functional characteristics of breast lesions using near-infrared (NIR) light to calculate the optical properties (scattering and absorption coefficients) of breast tissues. In this study, we have developed a NIR diffuse optical breast scanning (DOB-Scan) probe and evaluated a new imaging method based on a modified diffusion equation (MDE) for breast tumor localization. The probe is applied to breast phantoms to collect reflectance or the intensity of backscattered light. To measure the optical properties of the phantoms, we also calculated the reflectance theoretically, where we initially utilized the original diffusion equation (DE) to arrive at the theoretical reflectance. However, the DE algorithm has several limitations, which require modification of the DE formula to match the experimental results before obtaining the optical properties. Finally, the imaging algorithm is used to map the optical properties to cross-sectional images, which can localize the abnormalities in the breast phantoms. These findings suggest the DOB-Scan probe has valuable potential for breast cancer detection and diagnosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.008 | 0.003 |
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".