A new approach to analysis of RF ultrasound echo signals for tissue characterization: animal studies
Bibliographic record
Abstract
We present the results of an animal tissue characterization study to demonstrate the effectiveness of a novel approach in collecting and analyzing ultrasound echo signals. In this approach, we continuously record RF echo signals backscattered from a tissue sample, while the imaging probe and the tissue are fixed in position. The continuously recorded RF data generates a time series of RF signal samples. The Higuchi fractal dimension of the resulting time series at each spatial coordinate of the RF frame, averaged over a region of interest, serves as our tissue characterizing feature. The proposed feature is used along with Bayesian classifiers and feed-forward neural networks to distinguish different types of animal tissue. Pairwise classification of four different types of animal tissue are performed. Accuracies are in the range of 68%-96% and are significantly higher than the natural split of the data. The promising results of this study show that analysis of RF time series as proposed here, can potentially give rise to effective measures for ultrasound-based tissue characterization.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".