Improving Calibration of Black-Box Radiology AI Using Test-Time Augmentation

arXiv:2609.29931 · cs.LG, cs.HC · Submitted 2026-09-24 · Read on arXiv

cs.LG, cs.HC

Submitted: 2026-09-24

Updated: 2026-09-26

Terminology

• Calibration — 43× in this paper · explained in 7 episodes
• Aggregation — 22× in this paper · explained in 1 episode
• Robustness — 6× in this paper · explained in 5 episodes
• Model-Agnostic — 4× in this paper · explained in 2 episodes
• AUC — 3× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Failure Modes — 3× in this paper · explained in 3 episodes
• Test-time augmentation (TTA) — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Expected Calibration Error — 2× in this paper
• Foundation Model — 2× in this paper · explained in 3 episodes
• Greedy Policy Search — 2× in this paper
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Aleatoric Uncertainty — 1× in this paper · explained in 5 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Monte Carlo dropout — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Negative Log-Likelihood — 1× in this paper
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks
• A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
• Robustness Testing of Black-Box Models Against CT Degradation Through Test-Time Augmentation

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