MétaCan
Menu
Back to cohort
Record W2068949217 · doi:10.1118/1.3065066

Measurement of compressed breast thickness by optical stereoscopic photogrammetry

2009· article· en· W2068949217 on OpenAlexaff
Albert H. Tyson, Gordon E. Mawdsley, Martin J. Yaffe

Bibliographic record

VenueMedical Physics · 2009
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsSunnybrook HospitalHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMammographyImaging phantomCalibrationPaddleStereoscopyMaterials sciencePhotogrammetryBiomedical engineeringOpticsMedicineNuclear medicineComputer scienceComputer visionMathematicsPhysicsBreast cancer

Abstract

fetched live from OpenAlex

The determination of volumetric breast density (VBD) from mammograms requires accurate knowledge of the thickness of the compressed breast. In attempting to accurately determine VBD from images obtained on conventional mammography systems, the authors found that the thickness reported by a number of mammography systems in the field varied by as much as 15 mm when compressing the same breast or phantom. In order to evaluate the behavior of mammographic compression systems and to be able to predict the thickness at different locations in the breast on patients, they have developed a method for measuring the local thickness of the breast at all points of contact with the compression paddle using optical stereoscopic photogrammetry. On both flat (solid) and compressible phantoms, the measurements were accurate to better than 1 mm with a precision of 0.2 mm. In a pilot study, this method was used to measure thickness on 108 volunteers who were undergoing mammography examination. This measurement tool will allow us to characterize paddle surface deformations, deflections and calibration offsets for mammographic units.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.014
GPT teacher head0.258
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations48
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueMedical PhysicsSame topicDigital Radiography and Breast ImagingFrench-language works237,207