Parathyroid Gland Detection Based on Multi-Scale Weighted Fusion Attention Mechanism
Bibliographic record
Abstract
While deep learning techniques, such as Convolutional neural networks (CNNs), show significant potential in medical applications, real-time detection of parathyroid glands (PGs) during complex surgeries remains insufficiently explored, posing challenges for surgical accuracy and outcomes. Previous studies highlight the importance of leveraging prior knowledge, such as shape, for feature extraction in detection tasks. However, they fail to address the critical multi-scale variability of PG objects, resulting in suboptimal performance and efficiency. In this paper, we propose an end-to-end framework, MSWF-PGD, for Multi-Scale Weighted Fusion Parathyroid Gland Detection. To improve accuracy and efficiency, our approach extracts feature maps from convolutional layers at multiple scales and re-weights them using cluster-aware multi-scale alignment, considering diverse attributes such as the size, color, and position of PGs. Additionally, we introduce Multi-Scale Aggregation to enhance scale interactions and enable adaptive multi-scale feature fusion, providing precise and informative locality information for detection. Extensive comparative experiments and ablation studies on the parathyroid dataset (PGsdata) demonstrate the proposed framework’s superiority in accuracy and real-time efficiency, outperforming state-of-the-art models such as RetinaNet, FCOS, and YOLOv8.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".