Clinical Report: Enhancing Confidence Calibration in Surgical Visual Question Answering
Overview
This report presents a novel Scene Graph-Guided Uncertainty Decomposition (SG-UD) framework aimed at improving confidence calibration in surgical visual question answering (VQA). The proposed method achieves an overall accuracy of 63.58% and significantly enhances interpretability of uncertainty in surgical contexts.
Background
Reliable confidence estimation in surgical VQA is critical due to the high stakes involved in clinical decision-making. Traditional methods often oversimplify uncertainty, failing to account for the hierarchical nature of surgical scenes. This can lead to overconfident errors that may compromise patient safety.
Data Highlights
Metric
Value
Overall Accuracy
63.58%
Expected Calibration Error
16.97%
Risk-Coverage AUC
0.0861
Key Findings
The SG-UD framework decomposes uncertainty into object-level, relation-level, and scene-level components.
It incorporates Dirichlet-based calibration to enhance probabilistic quality.
Performance improvements were noted across all question types, particularly for relation-type questions (+0.92% over MCAN).
Ablation studies indicated that uncertainty decomposition contributed most to answer accuracy (+0.70%).
The framework provides more granular uncertainty interpretability compared to traditional methods.
Clinical Implications
The SG-UD framework offers a more nuanced approach to uncertainty in surgical VQA, which may enhance decision support systems in clinical settings. By improving confidence calibration, it aims to reduce the risk of overconfident errors in surgical contexts.
Conclusion
The findings suggest that a structured approach to uncertainty modeling can significantly improve the reliability and interpretability of surgical visual question answering systems.
