Resolution and SNR effects on carotid plaque classification
Bibliographic record
Abstract
Multicontrast-weighted MRI, which is increasingly being used in combination with automatic classification algorithms, has the potential to become a powerful tool for assessing plaque composition. The current literature, however, does not address the relationship between imaging conditions and segmentation viability well. In this study 13 carotid endarterectomy samples were imaged with a 156-microm in-plane resolution and high signal-to-noise ratio (SNR) using proton density (PD), T1, T2, and diffusion weightings. The maximum likelihood (ML) algorithm was used to classify plaque components, with sets of three contrast weighting intensities used as features. The resolution and SNR of the images were then degraded. Classification accuracy was found to be independent of in-plane resolution between 156 microm and 1250 microm, but dependent on SNR. Accuracy decreased less than 10% for degradation in SNR down to 25% of original values, and decreased sharply thereafter. The robustness of automatic classifiers makes them applicable to a wide range of imaging conditions, including standard in vivo carotid imaging scenarios.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".