Thermal-driven biomarkers for breast cancer screening using dynamic infrared imaging modality
Bibliographic record
Abstract
In this study, we delve into the applications of infrared based diagnostic system for early diagnosis of breast cancer or symptomatic patients.We used low-rank sparse Non-negative matrix factorization (NMF) to select the main bases of the thermal images to determine the subsurface thermal heterogeneous patterns in these sets.For that, 55 participants for infrared breast screening selected from Database for Mastology Research (DMR) dataset with symptomatic and healthy participants.We calculate five derived properties of the breast area (contrast, correlation, dissimilarity, homogeneous, and energy) using thermal level co-occurrence matrices (TLCMs) and train a logistic regression to stratify between healthy and symptomatic patients.We compared the ability of sparse-NMF to the state-of-the-art thermographic approaches such as principal component analysis/thermography (PCT), candid covariance-free incremental principal component thermography (CCIPCT), Sparse PCT, non-negative matrix factorization (NMF).Results indicate significant performance for Sparse-NMF (DMR: 74.1%).The results indicate considerable performance sparse-NMF, which conclusively indicates promising performance in terms of the accuracy and the robustness as a confirmation for the outlined properties.
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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 | 0.002 |
| 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) | 0.001 | 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".