Unsupervised Learning for Breast Abnormality Detection using Thermograms
Bibliographic record
Abstract
Breast cancer is the most diagnosed cancer in women globally. Thermography, an adjunctive tool to mammography, may be used for early detection of breast abnormality. The existing supervised learning techniques in the literature for abnormality detection using breast thermogram images require a big training set of labeled samples for adequate learning. However, labeling a large dataset is a resource-intensive and time-consuming process. Therefore, this work proposes a novel unsupervised learning methodology that employs a gradient-based local feature descriptor for abnormality detection using breast thermograms. Different measures of similarity (or distance) between the histogram of oriented gradients features (HoG, a gradient-based local feature descriptor) of the left and the right breast of an individual are used to identify the asymmetry between both breasts. The distances between the HoG features of both breasts are clustered using an unsupervised clustering algorithm called the$K$-means algorithm. The cluster with a greater centroid value (higher mean of distance values) indicates asymmetry and is labeled as abnormal. The red-plane of a thermogram containing the regions of higher temperature is extracted for enhanced gradient information and thereby, for enhanced detection of breast abnormality. This work pioneers the application of asymmetry-based unsupervised learning for the detection of breast abnormality in thermograms and achieves an accuracy of 86.52%.
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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 | 0.001 |
| 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".