Deep Model Projected Statistical Features for Homodyned K-Distribution Parameters Estimation
Bibliographic record
Abstract
Quantitative ultrasound (QUS) aims to find properties of scatterers which are highly correlated with the tissue microstructure. The homodyned K-distribution is one of the distributions that can model the envelope of RF data under diverse scattering conditions. The parameters of this distribution, the scattering clustering (α) and the ratio of coherent to diffuse scattering amplitude ratio (k) (which we refer to as HK parameters) are considered as valuable QUS parameters for tissue characterization in diagnostic ultrasound. Statistical features from the envelope of the backscattered radiofrequency (RF) data such as point-wise signal-to-noise ratio (SNR), skewness, kurtosis, and the log-based moments have been utilized to estimate HK parameters. Iterative optimization methods or table search can be used to estimate HK parameters from statistical features by comparing the estimated statistical features with theoretical values. In order to obtain HK parameters from the region of interest (ROI), a patch around the sample of interest is selected where the statistical features are calculated. A larger patch size provides more samples for statistical feature calculation, but increases the heterogeneity within the patch which might result in the failure of the optimization method especially for real tissues. Smaller patch size results in deviation of the statistical features from their theoretical values, causing error in the estimated HK parameters. The theoretical values of these statistical features lie in a low dimensional hyperplane since the feasible HK parameters are in low-dimensional manifold and have lower dimension than the statistical features. In this paper, we propose a model projection autoencoder inspired by a denoising autoencoder to project noisy statistical features into the hyperplane of expected values. The reconstructed features can be employed to estimate HK parameters by using any HK parameter estimator.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".