Evaluating Uncertainty in Deep Q-Network Ensembles forTrustworthy Anomaly Detection in Medical Imaging
Bibliographic record
Abstract
Reliable anomaly detection is crucial for safe AI deployment in clinical imaging, yet most systems offer limited insight into prediction uncertainty or failure modes—key factors in medical decision-making. We analyze the uncertainty characteristics of a patch-level Deep Q-Network anomaly detection framework (DQN_AD) for brain MRI, trained with few annotated abnormal cases and designed to generalize to highly imbalanced clinical datasets. Our study links uncertainty to model errors, calibration, anomaly scores, spatial correspondence with ground truth, and selective evaluation. Results show that high-uncertainty predictions consistently coincide with error-prone regions, providing a strong signal for identifying potential failures. This study establishes the foundation for uncertainty-aware, reinforcement learning–based anomaly detection models that enhance reliability, interpretability, and clinical usability in large-scale MRI analysis.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".