Evaluation of Balanced Ultrasound Breast Imaging Under Three Density Profile Assumptions
Bibliographic record
Abstract
A balanced inverse scattering algorithm for ultrasonic breast imaging is developed to simultaneously reconstruct quantitative images of the breast's ultrasonic properties. These properties are the inhomogeneous compressibility, attenuation, and density. Three scenarios are considered for this inversion algorithm. First, all the properties are assumed to be independent. The assumption of a linear relation between the contrast of compressibility and inverse density is then considered in the second scenario, whereas the density variation is neglected in the third scenario. The image corresponding to the attenuation is of particular importance because breast tumors can be better identified using this property in comparison with compressibility and density images. However, this contrast is often poorly reconstructed because the magnitude of this contrast in the mathematical formulation of the problem is generally smaller than the magnitude of the contrasts of the other two properties. To overcome this problem, a novel balancing method is applied to all these three inversion algorithms so as to enhance the reconstruction results. Using synthetic data from MRI-based breast models, it is demonstrated that the use of the proposed balancing scheme enhances the reconstruction results of all these algorithms and, in particular, enhances their reconstructed images corresponding to the attenuation profile.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".