Optimal thermomic biomarkers for early diagnosis of breast cancer
Bibliographic record
Abstract
Dynamic thermography is a well-established diagnostic tool for breast cancer screening that can be used in conjunction with mammography and clinical breast examination (CBE). Thermographic imaging biomarkers, known as thermomics, have been shown to detect vasodilation in breast tissue, indicating abnormalities and lesions. Heterogeneous thermal patterns also reveal angiogenesis or the formation of new blood vessels. This study applied thermal imaging biomarkers, and thermographic imaging, for breast cancer screening. We applied two low rank embedding approaches, Gaussian and Bell embedding, to obtain the optimal thermomics with the help of elbow method, which resulted in finding breast thermal heterogeneity. Non-negative Matrix Factorization (NMF) was used to create a low-ranked representation of thermal images. High dimensional radiomics and thermomics were then extracted, and feature abundance was reduced using spectral clustering. The best results of the Deep semiNMF with Bell embedding method combining clinical information and demographics yield 81.6% (±3.9%). The model was trained with constant hyperparameters setting across the comparison to predict abnormality, and the results demonstrated promising preliminary performance. Optimal biomarkers have the potential to preserve thermal heterogeneity, leading to early detection of breast cancer, and can serve as a non-invasive tool to aid CBE. Codes corresponded with this proceeding can be found at the following GitHub repository: https://github.com/BardiaYo/SPIEThermosense2023.git
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".