Diagnostic Biomarker for Breast Cancer Applying Rayleigh Low-Rank Embedding Thermography
Bibliographic record
Abstract
Thermography has found extensive application as a supplementary diagnostic tool in breast cancer diagnosis, notably complementing the clinical breast exam (CBE). Within dynamic thermography, matrix factorization methods have demonstrated their utility in accentuating thermal heterogeneities by generating thermal basis vectors. A significant challenge in such approaches is to identify the leading thermal basis vector that effectively captures predominant thermal patterns. Embedding methods are used to fuse multiple projected basis vectors onto a single basis for the extraction of the thermal features, known as thermomics. In this study, we introduce Rayleigh embedding to project thermal basis vectors obtained from factorization techniques into a lower-dimensional space, highlighting thermal patterns. This enhances the reliability of the thermal system, thereby assisting in CBE. The best results of the embedding method combining clinical information and demographics yield 82.9% (66.7%, 86.7%) using a random forest. The results demonstrated promising preliminary outcomes, leading to the early detection of breast abnormalities, and can serve as a non-invasive tool to aid CBE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".