Detection of architectural distortion in prior mammograms using measures of angular distribution
Bibliographic record
Abstract
We present methods for the detection of architectural distortion in mammograms of interval-cancer cases taken prior to the diagnosis of breast cancer using measures of angular distribution derived from Gabor filter responses in magnitude and angle, coherence, orientation strength, and the angular spread of power in the Fourier spectrum. A total of 4224 regions of interest (ROIs) were automatically obtained using Gabor filters and phase portrait analysis from 106 prior mammograms of 56 interval-cancer cases with 301 ROIs related to architectural distortion, and from 52 mammograms of 13 normal cases. Images of coherence and orientation strength were derived from the Gabor responses in magnitude and orientation. Each ROI was represented by the entropy of the angular histogram composed with the Gabor magnitude response, angle, coherence, and orientation strength; the entropy of the angular spread of power in the Fourier spectrum was also computed. Using stepwise logistic regression for feature selection and the leave-one-image-out method in feature selection and pattern classification, the area under the receiver operating characteristic curve of 0.76 was obtained with an artificial neural network based on radial basis functions. Analysis of the free-response receiver operating characteristics indicated 82% sensitivity at 7.2 false positives per image.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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 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".