SU‐FF‐I‐22: An Inward Mammilla Detection Algorithm for Analysis of Skin‐Line Retraction
Bibliographic record
Abstract
Purpose: A specific algorithm is designed for the detection of the mammilla to locate the inward mammilla position along the breast skin‐line in mammograms. The position of the inward mammilla can assist in the analysis of focal retraction near the nipple. Method and Materials: Between the breast skin‐line and the fibro‐glandular tissue is a zone of fatty peripheral tissue, which appears with low gray‐levels on mammograms. Due to the mammary glands connecting to the mammilla, the gray‐level in the fatty zone near the mammilla will be higher. We define a fatty peripheral zone (Zf) of 40 pixels width (8mm) parallel to the skin‐line on mammograms. A disk mask of diameter 40 pixels, KP, tangential to the skin‐line boundary point P and rolling in the zone Zf, is used to obtain a mammilla index value for P. A mammilla index (IP) for P is defined as the average gray‐level of the pixels in both Zf and the current mask Kp. Then, three highest values, It1 (highest), It2 (second highest), and It3 (third highest), corresponding to position indexes Pt1, Pt2, and Pt3 on the skin‐line, are found on the curve of IP. If the differences between Pt1 and Pt2, as well as between Pt1 and Pt3, are larger than a threshold T1, and the difference between Pt2 and Pt3 is less than another threshold T2, the mammilla position is defined as the average of Pt2 and Pt3; otherwise the mammilla position is defined as Pt1. Empirically, we selected T1=90 and T2=36. Results: We have tested our algorithm on 40 mammograms from the MiniMIAS database with inward nipples, and our method achieved accurate detection of the mammilla position on each image. Conclusion: The proposed algorithm for the detection of the inward mammilla position gave accurate results on the mammograms tested.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".