Comparison of attenuation compensation methods in the estimation of the backscatter coefficient of breast cancer
Bibliographic record
Abstract
Goal: This study compares two strategies to compensate for attenuation in the estimation of the backscatter coefficient σ of breast cancer Methods: Attenuation compensation was performed using local attenuation estimates from a constrained Spectral-Log-Difference (CSLD) method, and average estimates from a dynamic programming (DP)-based regularized method. First, bias and linearity of σ estimates from both strategies were evaluated in a Gammex 410SCG phantom with inclusions with known σ imaged at 8 MHz on a Verasonics Vantage 128 scanner. Then, the strategies were compared in terms of the contrast-to-noise ratio (CNR) between adipose tissue and invasive ductal carcinoma. σ estimates were obtained from 10 patients with biopsy-confirmed IDCs imaged invivo with a 12L4 transducer on a Siemens Acuson S2000 as part of an IRB-approved protocol at the National Institute of Cancerology in Mexico City. DP σ estimates provided smaller bias and better linearity compared to estimates of CSLD. In the in vivo study, DP σ estimates produced negative IDC-vs-adipose CNR that was significantly different from zero (p = 0.002) and agreed with the hypoechoic appearance of IDC. [Work supported by CONACYT Ciencia de Frontera 1311307, ASA international student support, and UNAM PAPIIT IA102320. We thank Siemens Mexico for scanner loan.]
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".