Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper introduces a calibrated Bayesian deep learning framework for medical imaging decision support, addressing the critical need for reliable uncertainty quantification in AI-assisted diagnostics. The framework combines a novel Confidence-Uncertainty Boundary Loss (CUB-Loss) during training, which penalizes high-confidence errors and low-confidence correct predictions, with a post-hoc Dual Temperature Scaling (DTS) strategy to refine the posterior distribution. Validated on pneumonia screening, diabetic retinopathy detection, and skin lesion identification, the approach demonstrates improved calibration, robust performance in data-scarce scenarios, and effectiveness on imbalanced datasets.
AI-powered medical image analysis can be made more reliable and trustworthy by explicitly training models to be uncertain when they're likely to be wrong.
In critical decision support systems based on medical imaging, the reliability of AI-assisted decision-making is as relevant as predictive accuracy. Although deep learning models have demonstrated significant accuracy, they frequently suffer from miscalibration, manifested as overconfidence in erroneous predictions. To facilitate clinical acceptance, it is imperative that models quantify uncertainty in a manner that correlates with prediction correctness, allowing clinicians to identify unreliable outputs for further review. In order to address this necessity, the present paper proposes a generalizable probabilistic optimization framework grounded in Bayesian deep learning. Specifically, a novel Confidence-Uncertainty Boundary Loss (CUB-Loss) is introduced that imposes penalties on high-certainty errors and low-certainty correct predictions, explicitly enforcing alignment between prediction correctness and uncertainty estimates. Complementing this training-time optimization, a Dual Temperature Scaling (DTS) strategy is devised for post-hoc calibration, further refining the posterior distribution to improve intuitive explainability. The proposed framework is validated on three distinct medical imaging tasks: automatic screening of pneumonia, diabetic retinopathy detection, and identification of skin lesions. Empirical results demonstrate that the proposed approach achieves consistent calibration improvements across diverse modalities, maintains robust performance in data-scarce scenarios, and remains effective on severely imbalanced datasets, underscoring its potential for real clinical deployment.