Mammographic classification with multiple global features
Bibliographic record
Abstract
In this thesis several global mammographic features were examined for their ability to classify the mammograms into (1) classes based on the proportion of dense tissue; (2) normal/abnormal groups. A set of 240 digitised mammograms was obtained from the Digital Database for Screening Mammography from the University of South Florida. The database was composed of mammograms that were digitized using one of three high resolution x-ray digitisers. It was necessary for the images to be corrected for three systematic differences between the x-ray digitisers: the resolution, the slope of the calibration curve and non-linearities in the calibration curve. A simple correction was also made for differences in the mammographic technique by adjusting the histogram of the breast shadow. The breast shadow was then segmented using a semi-automatic procedure and several mammographic properties were extracted: global moments of the histogram, the average local moments calculated for ~3 x 3 mm2 regions covering the breast shadow, subregions of the global histogram, multifractal dimensions and the texture energy, entropy and inertia calculated for the wavelet transform of the image. The classification accuracy, when considering the density grades, was consistently ~40% correct and independent of the properties used in the classifier. When classifying into normal/abnormal groups, the regional moments, histogram sub-regions and the multifractal dimensions all had approximately the same performance at ~60% correctly classified cases, While the global moments classified ~70% of the cases correctly. The texture energy, entropy and inertia also had approximately the same performance but at ~80-85% correct. In addition, the classifiers exhibited no significant change in classification performance for variations in age for any of the examined properties with ' p' = 0.001. The texture features resulted in the highest classification accuracy. The results may show some residual dependence on the x-ray digitiser but the small sample size precluded any definitive conclusions regarding the influence of the scanners. Overall, a classifier using six texture inertia features exhibited the best overall classification accuracy with minimal age dependence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".