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BEYOND AUROC: Evaluating Temporal Stability, False-Positive Load, and Uncertainty Calibration in Capsule Endoscopy Video AI
BEYOND AUROC: Evaluating Temporal Stability, False-Positive Load, and Uncertainty Calibration in Capsule Endoscopy Video AI
BEYOND AUROC: Evaluating Temporal Stability, False-Positive Load, and Uncertainty Calibration in Capsule Endoscopy Video AI
Conference:
Conference:
IEEE ISBI 2026 (VideoAI Workshop)
IEEE ISBI 2026 (VideoAI Workshop)

Keywords:
Keywords:
Capsule Endoscopy, Medical Video AI, Temporal Stability, False Positive Analysis, Uncertainty Calibration, Reliability Evaluation
Capsule Endoscopy, Medical Video AI, Temporal Stability, False Positive Analysis, Uncertainty Calibration, Reliability Evaluation
Wireless capsule endoscopy operates under photon-limited conditions where spatially varying illumination attenuation and resolution degradation obscure fine anatomical structures. Conventional super-resolution methods may amplify unstable high-frequency content, producing visually sharp yet structurally unreliable outputs. We propose RCD-SR, a reliability-conditioned diffusion framework that jointly couples illumination stabilization and super-resolution via spatial confidence modulation. A Retinex-inspired decomposition estimates illumination and recoverable structure, while a confidence map regulates generative refinement during diffusion sampling, constraining amplification in low-signal regions. Without paired ground truth, evaluation is performed using no-reference and relative-reference metrics at ×4 upsampling. RCD-SR demonstrates improved structural alignment, controlled frequency behavior, and reduced artifact amplification compared to existing methods. This approach provides a principled solution for hallucination-aware reconstruction in photon-limited medical imaging.
Wireless capsule endoscopy operates under photon-limited conditions where spatially varying illumination attenuation and resolution degradation obscure fine anatomical structures. Conventional super-resolution methods may amplify unstable high-frequency content, producing visually sharp yet structurally unreliable outputs. We propose RCD-SR, a reliability-conditioned diffusion framework that jointly couples illumination stabilization and super-resolution via spatial confidence modulation. A Retinex-inspired decomposition estimates illumination and recoverable structure, while a confidence map regulates generative refinement during diffusion sampling, constraining amplification in low-signal regions. Without paired ground truth, evaluation is performed using no-reference and relative-reference metrics at ×4 upsampling. RCD-SR demonstrates improved structural alignment, controlled frequency behavior, and reduced artifact amplification compared to existing methods. This approach provides a principled solution for hallucination-aware reconstruction in photon-limited medical imaging.
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