Multi scale classification approach for coronary artery detection from X-ray angiography
Bibliographic record
Abstract
X-ray angiography is currently the gold standard for navigation guidance during percutaneous coronary interventions. From X-ray angiography, robust automatic detection of coronary arteries would be of great interest during cardiac interventions. Multi scale Hessian-based filtering was proven successful to automatically detect vessels from X-ray angiography. However, other anatomical structures interfere greatly with the detection process and the result still contains many false positives. The goal of the project is to propose a novel machine learning-based method to improve Hessian-based coronary artery detection from X-ray angiography. The proposed method divides Hessian-filtered images in patches, uses feature extraction with a contour profiling algorithm, and classifies using Support Vector Machines. The method is applied recursively on the detected connected components using patches of different sizes to define the arteries. This scheme allows an improvement of robustness against noise and imaging artifacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".