GPU accelerated implementation of ultrasound radio-frequency time series analysis
Bibliographic record
Abstract
The ultrasound radio-frequency (RF) time series method has been shown to be an effective approach for accurate tissue classification and cancer detection. Previous studies of the RF time series method were based on a serial MATLAB implementation of feature calculation that involved long running times. Clinical applications of the RF time series method require a fast and efficient implementation that enables realistic imaging studies within a short time frame. In this paper, a parallel implementation of the RF time series method is developed to support clinical ultrasound imaging studies. The parallel implementation uses a Graphics Processing Unit (GPU) to compute the tissue classification features of the RF time series method. Moreover, efficient graphical representations of the RF times series features are obtained using the Qt framework. Tread computing is used to concurrently compute and visualize the RF time series features. The parallel implementation of the RF time series is evaluated for various configurations of number of frames and number of scan lines per frame acquired in an imaging study. Results demonstrate that the parallel implementation enables imaging of tissue classification at interactive time. A typical RF time series of 128 frames and 128 scan lines per frame, the parallel implementation be processed in 0.8128 ± 0.0420 sec.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".