Accurate estimation of compressed breast thickness in mammography
Bibliographic record
Abstract
The determination of volumetric breast density (VBD) from mammograms requires an accurate knowledge of the thickness of the compressed breast. Previously, the authors described a technique for measuring local thicknesses using optical stereoscopic photogrammetry [A. H. Tyson, G. E. Mawdsley, and M. J. Yaffe, "Measurement of compressed breast thickness by optical stereoscopic photogrammetry," Med. Phys. 36(2), 569-576 (2009)]. Here, the authors describe the use of this tool to guide the development of a simpler, more practical field technique for the estimation of breast thickness and test its accuracy. Phantoms were constructed having similar shapes and compression characteristics to breasts of different sizes. These phantoms were compressed at different forces on several types of mammography units and their thickness under compression was measured using optical stereoscopic photogrammetry at many points of contact with the compression plate. A prediction equation was developed that uses the readout of compressed thickness and compression force provided by the mammography system to estimate local breast thickness. Using this approach, systems can be calibrated to an accuracy of better than 5 mm in thickness using a simple test object compared to an error of up to 15 mm associated with using only the thickness readout of the mammography machine. On the systems tested, the estimated value of VBD obtained using this method is significantly reduced from that determined using the constant thickness reported by the mammography machine.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".