Single domain generalized polyp detection in colonoscopy scene utilizing vision foundation models

Deep learning models for colorectal polyp detection often face distribution shift issues due to dataset artifacts, variations in imaging equipment, and other clinical factors, which result in unreliable performance in real-world settings. To address this, several generalizable object detectors have been proposed, aiming to train models that can generalize across multiple target domains. However, existing approaches to generalizable detection typically rely on two main strategies: Unsupervised Domain Adaptation, which requires unlabeled data from the target domains, and Multi-Source Domain Generalization, which necessitates data from multiple source domains. These approaches are challenging to implement in clinical settings, where data privacy regulations limit the accessibility. This work proposes a novel approach for polyp detection in colonoscopy scenes under the more realistic setting of single domain generalization, called Generalizable Polyp Detection Transformer (GPDT). Our method leverages Vision Foundation Models for robust feature extraction and introduces a learnable-token-driven adapter mechanism to fine-tune these models with minimal additional parameters. This approach enables effective generalization across unseen clinical domains when only a single source domain is available for training. Extensive experiments on two multi-center polyp detection generalization benchmarks, PolypGen and REAL-Colon, show that GPDT achieves stronger performance compared with existing state-of-the-art methods across multiple target domains. Furthermore, we introduce an efficient variant, E-GPDT, that accelerates inference while preserving detection accuracy, yielding a favorable speed-accuracy trade-off under the evaluated edge-device configuration. E-GPDT is trained as a source-specific student distilled from adapter-enhanced GPDT teachers under the same single-source protocol, thereby connecting the high-capacity VFM-adapted detector and the lower-latency student detector within one unified pipeline. Our results demonstrate that adapting VFMs provides a promising pathway for improving cross-domain polyp detection generalizability.