UA-CP: Uncertainty-Aware Conformal Prediction for Reliable Endoscopic Lesion Detection

Reliable AI-assisted endoscopic lesion detection requires both accurate predictions and explicit uncertainty characterization, yet subtle appearances, ambiguous boundaries, and imbalanced category distributions challenge classification and localization. We propose UA-CP, a two-stage framework combining detector-adaptive Prior-Aware Training (PAT) with Dual Conformal Calibration (DCC). PAT incorporates category-frequency priors into classification objectives for proposal-based and dense detectors or assignment objectives for query-based detectors. DCC then constructs conformal category prediction sets using RAPS and simultaneous localization regions using conformalized quantile regression. Under exchangeability, these outputs provide finite-sample marginal coverage guarantees, with a joint lower bound obtained by the union bound. Across three complementary endoscopic benchmarks and four detector configurations, PAT generally improves detection performance, while DCC characterizes architecture-dependent trade-offs between uncertainty validity and efficiency. Results demonstrate that UA-CP generally improves detection utility while providing valid and informative category and localization uncertainty across diverse endoscopic detection settings.