ARIG-GCN: Anatomical Relationship and Isomorphic Graph Approximation Guided Graph Convolutional Network for Automated ASPECTS Scoring on Non-Contrast CT
Bibliographic record
Abstract
The Alberta Stroke Program Early CT Score (AS-PECTS) is a systematic method for assessing the extent of early ischemic changes on non-contrast CT (NCCT) of patients with acute ischemic stroke (AIS). The ASPECTS regions are anatomically and physiologically interconnected, making them suitable for analysis by graph neural networks. However, most existing methods fail to effectively use the relationships and bilateral differences. This study designs an Anatomical Relationship and Isomorphic Graph approximation guided Graph Convolutional Network for ASPECTS scoring on NCCT. Firstly, we propose a node construction guided by anatomical structures, i.e., utilizing the region anatomical relationship adjacency matrix of the ASPECTS regions to build the nodes. Secondly, to optimize the information propagation among nodes, we propose isomorphic graph approximation, utilizing edge learning, connectivity-based subgraph selection, and supervised isomorphic subgraph approximation to supervise the construction of isomorphic subgraphs. We validate our method on private AIS datasets which included NCCT scans of 257 AIS patients. The results show that the proposed method achieves interclass correlation coefficients of 0.8554 for total ASPECTS, and accuracy of 90.61% for dichotomized ASPECTS scoring (4), outperforming 9 state-of-the-art methods.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".