Work

Transformer-based endoscopic polyp detection with structural reparameterization and dynamic upsampling

Year: 2026

Type: article

Abstract: Accurate polyp detection is important for colorectal cancer prevention, while practical deployment requires reliable localization, manageable model complexity, and robust detection performance. This paper presents an improved RT-DETR-r18 detector for endoscopic polyp detection. The proposed C2f_RCB backbone construction embeds a UniRepLKNet-derived large-kernel reparameterization transformation within the C2f hierarchy, while the original DySample operator replaces the two nearest-neighbor upsampling layers in the top-down neck. A source-code audit shows that the K = 7 DilatedReparamBlock contains one 7 × 7 depthwise main branch and three auxiliary depthwise branches, namely 5 × 5 with dilation 1, 3 × 3 with dilation 2, and 3 × 3 with dilation 3. During deployment, these spatial branches are merged into an equivalent 7 × 7 depthwise convolution, and compatible normalization/affine terms are further folded into adjacent operators by the UniRepLKNetBlock deployment routine; SE, GELU, GRN normalization/scaling, channel projections, residual operations, and DySample remain explicit in the inference graph. Under the archived evaluation protocol, the complete model achieves 96.8% precision, 93.0% recall, 97.1% [email protected], and 79.5% [email protected]:0.95, with 13.90 million parameters and 46.6 GFLOPs. These values were obtained from an expanded-file split later found to contain augmented variants of the same original samples across subsets and should therefore be regarded as preliminary until leakage-free retraining is completed. The current evidence supports a compact, localization-oriented trade-off, but not a standardized runtime advantage, clinical readiness, or leakage-free generalization.

Source: Scientific Reports

DOI: 10.1038/s41598-026-77142-z