A Machine Learning Approach: NIR Scattering Data Analysis for Breast Cancer Detection and Classification
Bibliographic record
Abstract
Current breast cancer imaging modalities have some limitations. Diffuse optical imaging has shown significant potential for breast cancer detection and treatment response monitoring and can be an alternative, if not a replacement, to conventional imaging modalities. In this study, we have shown the diffuse optical breast-scanning (DOB-Scan) probe, utilizing a machine learning model, can discriminate between normal and malignant breast tissues. We have applied our model to fifteen breast cancer patients' datasets to predict patients' cancerous and normal breasts' optical properties. The regression model based on the correlation between the radial reflectance and the optical tissue properties is applied to predict the patients' scattering coefficients. As the model is a classifier, significant differences in the scattering coefficients of normal and malignant tissues have been reported. In addition, the scattering values for the longitudinal data have been calculated to assess patients' chemotherapy responses. These findings suggested that the diffuse optical scattering coefficient is a promising early marker of breast cancer. In addition, tumor features such as tumor heterogeneity, absorption concentration, initial tumor volume, and residual cancer after therapy could be predicted by this method.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".