JS divergence Weibull embedding for very early breast cancer diagnosis
Bibliographic record
Abstract
Dynamic thermography has emerged as a reliable adjunctive tool in breast cancer screening, complementing traditional methods such as mammography and clinical breast examination (CBE). Thermographic imaging, yielding thermal biomarkers termed thermomics, exhibits promise in identifying vasodilation within breast tissue, signaling potential abnormalities and lesions. Moreover, the observation of heterogeneous thermal patterns facilitates the detection of angiogenesis, the process of new blood vessel formation. This study investigates the application of thermal imaging biomarkers and thermographic imaging in breast cancer screening by applying Jensen‐Shannon (JS) divergence calculation on the low rank representation of thermal image sets obtained through Uniform Manifold Approximation and Projection (UMAP) to select images best for feature extraction and employing Weibull embedding to highlight heterogenous patterns in the thermal sequences. The results are used to extract high‐dimensional thermomics and spectral clustering is used to reduce feature abundance. The model, trained with consistent hyperparameters across comparisons, demonstrated favorable preliminary performance in predicting abnormality. These optimal biomarkers have the potential to capture thermal heterogeneity effectively, thereby facilitating the early detection of breast cancer and serving as a non‐invasive aid to CBE.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".