Design and Application of Signal Modeling, Segmentation and Classification Methods for High-Frequency Ultrasound Backscatter Signals
Bibliographic record
Abstract
In this study, we explore the possibility of monitoring program cell death (apoptosis) and classifying clusters of apoptotic cells based on the changes in high frequency ultrasound backscatter signals from these cells. One of the hallmarks of cancer is that the fail [sic] in the apoptosis mechanism in cells. Therefore this research carries the promise of designing more refined and more effective cancer therapies. The ultrasound signals are modeled through the Autoregressive (AR) modeling technique. The proper model order is calculated by tracking the error criteria derived from statistical properties of the original and modeled signal. In the next stage, five machine learning classifiers are developed to classify backscatter signals based on their AR coefficients. In clinical applications ultrasound backscatter signals from tissues and tumors are most likely to be non-stationary. Therefore analyzing such signals requires signal segmentation techniques. We developed recursive least square lattice filter for adaptive segmentation of ultrasound backscatter signals from multiple cell types into blocks of stationary segments, and model and classify the segments individually. In this thesis we demonstrate the accuracy of modeling, segmentation and classification techniques to detect signals from different cell pellets based on the signal processing and machine learning techniques.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".