Domain Specific Transporter Framework to Detect Fractures in Ultrasound
Bibliographic record
Abstract
Ultrasound examination for detecting fractures is ideally suited for Emergency Departments (ED) as it is fast, safe (from ionizing radiation), has dynamic imaging capability, and is easily portable. High variability in manual assessment of ultra-sound has piqued research interest in automatic assessment using Deep Learning (DL). Most DL techniques are trained on large labeled datasets which is expensive and requires many hours of careful annotation. We propose an unsupervised, domain-specific transporter framework to identify relevant key points from ultrasound scans providing a concise geometric representation highlighting regions with high structural variation. We incorporate domain-specific information using instantaneous local phase (LP) which detects bone features. We validate the technique on wrist 3DUS videos obtained from 30 subjects each independently assessed by 3 readers to identify fractures. The saliency of key points detected is compared against manual assessment based on distance from relevant features. Our approach was able to accurately detect 180/250 bone regions. We expect this technique to increase the applicability of ultrasound in fracture detection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".