Adaptive learning of tissue reflectivity statistics and its application to deconvolution of medical ultrasound scans
Bibliographic record
Abstract
Image deconvolution is an important post-processing methodology allowing a substantial improvement it terms of both the resolution and contrast of medical ultrasound scans. Unfortunately, the intrinsic bandlimitedness of ultrasound scanners renders the problem of image deconvolution ill-posed, and, as a result, the latter rarely admits a stable and unique solution, unless properly regularized. To be successful, the regularization needs to properly reflect the actual reflectivity properties of insonified tissues. Unfortunately, the inherent complexity of biological tissues makes it extremely difficult (if at all possible) to justify the use of a single statistical model for the description of their acoustic reflectivity structure. Thus, for example, Gaussian and Laplacian statistical priors can provide adequate description of the characteristics of diffusive scattering and specular reflection, respectively, while yielding inaccurate estimates when either of the two is used exclusively for concurrent description of both reflectively types. To overcome this difficulty, we propose to use a concomitant scale estimation framework, which allows one to learn the parameters of a prior model along with its associated reflectivity properties directly from the data. The proposed method is formulated in the form of a convex optimization problem that admits a unique and efficiently computable solution.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".