Modeling scattering from cells and biological structures
Bibliographic record
Abstract
The goal of ultrasonic tissue characterization is to extract information over and above the information conventionally displayed on imaging instruments which is typically the backscatter strength (B-mode image). The driving hypothesis of our work is that as the ultrasound wavelength approaches the size of a cell, the backscatter characteristics become more sensitive to the cells' physical attributes (size, structure, and composition) and changes in the cell structure due to therapeutic intervention. A better understanding of the interaction of acoustic waves with cells is required to interpret the backscatter data. An overview of our efforts to model scattering from individual cells, cell ensembles, and in-vivo cancer will be presented. Theoretical results will be presented comparing scattering from individual cells with experimental data from a variety of cells in suspension, cell ensembles with pellets (compact aggregates of cells) and in-vivo tumors. It will be shown that for the tumors we have studied there is a very good correlation of scattering characteristics between cell pellets and in-vivo tumors, indicating that in the tumors studied cell structure was the factor that dominated the scattering response. The goal of this work is to use quantitative ultrasound methods for diagnosis and monitoring the response to therapy.
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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 | 0.002 | 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".