Handheld diffuse optical breast scanner probe for cross-sectional imaging of breast tissue
Bibliographic record
Abstract
Diffuse optical spectroscopy is a relatively new, noninvasive and nonionizing technique for breast cancer diagnosis. In the present study, we have introduced a novel handheld diffuse optical breast scan (DOB-Scan) probe to measure optical properties of the breast in vivo and create functional and compositional images of the tissue. In addition, the probe gives more information about breast tissue’s constituents, which helps distinguish a healthy and cancerous tissue. Two symmetrical light sources, each including four different wavelengths, are used to illuminate the breast tissue. A high-resolution linear array detector measures the intensity of the back-scattered photons at different radial destinations from the illumination sources on the surface of the breast tissue, and a unique image reconstruction algorithm is used to create four cross-sectional images for four different wavelengths. Different from fiber optic-based illumination techniques, the proposed method in this paper integrates multi-wavelength light-emitting diodes to act as pencil beam sources into a scattering medium like breast tissue. This unique design and its compact structure reduce the complexity, size and cost of a potential probe. Although the introduced technique miniaturizes the probe, this study points to the reliability of this technique in the phantom study and clinical breast imaging. We have received ethical approval to test the DOB-Scan probe on patients and we are currently testing the DOB-Scan probe on subjects who are diagnosed with breast cancer.
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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 | 0.001 |
| 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) | 0.003 | 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".