Tissue typing with ultrasound RF time series: phantom studies
Bibliographic record
Abstract
We report phantom studies on a new approach to ultrasound-based tissue typing. In the proposed approach, we continuously record RF echo signals backscattered from tissue, while the imaging probe and the tissue are fixed in position. The continuously recorded RF data generates a time series of echoes for each spatial sample of the RF signal. We use the spectral and fractal features of ultrasound RF time series averaged over a region of interest, along with support vector machine classifiers, for tissue typing. In this paper, the effects of two properties of tissue on RF time series are investigated: cell size and elasticity. We show that RF time series acquired from agar-gelatin based tissue mimicking phantoms, with difference only in the size of cell-mimicking glass beads, are distinguishable with statistically reliable accuracies up to 82.2%. Similar experiments using phantoms with different elastic properties did not result in consistently high classification accuracies. The results of this study confirm that the evident differences in microstructure of the cancerous versus normal tissue could play a role in the success of the proposed tissue typing method in detection of prostate cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".