Graph Representation of CTA Images for Spatially Aware Classification
Bibliographic record
Abstract
Coronary artery disease (CAD) remains among widely prevalent health challenges globally, and a leading cause of morbidity. We introduce an innovative methodology designed for CAD detection from computed tomography angiography (CTA) images and address some inherent limitations in conventional methods, such as classical machine learning and convolutional neural networks (CNNs). Our graph-based algorithms leverage graph neural networks (GNNs) to effectively model complex spatial dependencies in coronary arteries network. We develop an anatomically informed representation by constructing graphs from volumetric CTA images, and explicitly encode the localized radiomics features. Our framework maps the CAD detection as a graph classification problem, where we propagate spatial contexts through message-passing GNNs and the attention based global pooling gives CAD prediction scores. Comprehensive experimental evaluations on real-world datasets demonstrate that our proposed GNN-based approach has practical utility of radiomics data representation. Our ablation studies and baseline comparisons validate the applicability of GNNs in CTA imaging and establishes their potential as an efficient and robust diagnostic tool for non-invasive CAD assessment.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".