Detection of architectural distortion in prior mammograms using measures of angular dispersion
Bibliographic record
Abstract
Architectural distortion is a mammographic sign of breast cancer at an early stage. We propose methods for the characterization and detection of architectural distortion based on measures of spicularity and angular dispersion of the patterns in automatically detected regions of interest (ROIs) using Gabor filters and phase-portrait analysis. The novel features for the characterization of the spiculating patterns of architectural distortion include an index of convergence of spicules computed from the Gabor magnitude and coherence using the Gabor angle response; radially weighted difference and angle-weighted difference measures of the intensity, Gabor magnitude, and Gabor angle response; and the angle-weighted difference in entropy of spicules computed from the intensity, Gabor magnitude, and Gabor angle response. At first, 4,224 ROIs were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs, and from 52 mammograms of 13 normal cases using Gabor filters and phase-portrait analysis. Using the newly proposed features through feature selection and pattern classification, the best result achieved, in terms of the area under the receiver operating characteristic curve, is 0.76 with an artificial neural network based on radial basis functions. Free-response receiver operating characteristic analysis indicated a sensitivity of 0.90 at 6.3 false positives per patient.
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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".