Comparison of Different Color Spaces for Abnormality Detection in Breast Thermograms
Bibliographic record
Abstract
Breast cancer is one of the most diagnosed cancers in women worldwide. Thermography serves as a potential screening modality for early breast abnormality detection. A breast thermogram is a pseudo-colored RGB image that is obtained using a thermal palette. Different types of thermal palettes can be used for pseudo-coloring breast thermograms and may influence abnormality detection in RGB breast thermograms. Universal color spaces/ color models that are derived from RGB color space can be used to mitigate the influence of the choice of different thermal palettes. In this work, RGB breast thermograms are mapped to five different color spaces (namely YIQ color space, YCbCr color space, XYZ color space, LAB color space, and HSV color space). The performance of an existing novel, non-learning, threshold-based breast abnormality detection methodology using these mapped thermograms is investigated. An exhaustive channel-wise analysis of different color spaces is also explored to study the influence of each color space channel on breast abnormality detection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".