Accuracy of a Cascade Network for Semi‐Supervised Maxillary Sinus Detection and Sinus Cyst Classification
Bibliographic record
Abstract
OBJECTIVE: Maxillary sinus mucosal cysts represent prevalent oral and maxillofacial diseases, and their precise diagnosis is essential for surgical planning in maxillary sinus floor elevation. This study aimed to develop a deep learning-based pipeline for the classification of maxillary sinus lesions in cone beam computed tomography (CBCT) images to provide auxiliary support for clinical diagnosis. METHODS: This study utilized 45 136 maxillary sinus images from CBCT scans of 541 patients. A cascade network was designed, comprising a semi-supervised maxillary sinus area object detection module and a maxillary sinus lesions classification module. The object detection module employed a semi-supervised pseudo-labelling training strategy to expand the maxillary sinus annotation dataset. In the classification module, the performance of Convolutional Neural Network and Transformer architectures was compared for maxillary sinus mucosal lesion classification. The object detection and classification modules were evaluated using metrics including Accuracy, Precision, Recall, F1 score, and Average Precision, with the object detection module additionally assessed using Precision-Recall Curve. RESULTS: The fully supervised pseudo-label generation model achieved an average accuracy of 0.9433, while the semi-supervised maxillary sinus detection model attained 0.9403. ResNet-50 outperformed in classification, with accuracies of 0.9836 (sagittal) and 0.9797 (coronal). Grad-CAM visualization confirmed accurate focus on clinically relevant lesion features. CONCLUSION: The proposed pipeline achieves high-precision detection and classification of maxillary sinus mucosal lesions, reducing manual annotation while maintaining accuracy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".