Accurate polyp detection is critical for early colorectal cancer diagnosis. Although remarkable progress has been achieved in recent years, the complex colon environment and concealed polyps with unclear boundaries still pose severe challenges in this area. Existing methods either involve computationally expensive context aggregation or lack prior modeling of polyps, resulting in poor performance in challenging cases. In this paper, we propose the Enhanced CenterNet with Contrastive Learning (ECC-PolypDet), a two-stage training \& end-to-end inference framework that leverages images and bounding box annotations to train a general model and fine-tune it based on the inference score to obtain a final robust model. Specifically, we conduct Box-assisted Contrastive Learning (BCL) during training to minimize the intra-class difference and maximize the inter-class difference between foreground polyps and backgrounds, enabling our model to capture concealed polyps. Moreover, to enhance the recognition of small polyps, we design the Semantic Flow-guided Feature Pyramid Network (SFFPN) to aggregate multi-scale features and the Heatmap Propagation (HP) module to boost the model's attention on polyp targets. In the fine-tuning stage, we introduce the IoU-guided Sample Re-weighting (ISR) mechanism to prioritize hard samples by adaptively adjusting the loss weight for each sample during fine-tuning. Extensive experiments on six large-scale colonoscopy datasets demonstrate the superiority of our model compared with previous state-of-the-art detectors.

翻译：准确的息肉检测对于早期结直肠癌诊断至关重要。尽管近年取得了显著进展，但复杂的结肠环境及边界模糊的隐蔽性息肉仍对此领域构成严峻挑战。现有方法或需要高昂计算成本进行上下文聚合，或缺乏对息肉的先验建模，导致在复杂病例中性能欠佳。本文提出基于对比学习增强的CenterNet（ECC-PolypDet），这是一种两阶段训练与端到端推理框架，通过利用图像与边界框标注训练通用模型，并基于推理得分进行微调以获得最终的鲁棒模型。具体而言，我们在训练过程中实施框辅助对比学习（BCL），以最小化前景息肉与背景的类内差异、最大化类间差异，使模型能够捕捉隐蔽性息肉。此外，为增强对小息肉识别能力，我们设计了语义流引导特征金字塔网络（SFFPN）以聚合多尺度特征，并引入热力图传播（HP）模块提升模型对息肉目标的关注度。在微调阶段，我们提出交并比引导样本重加权（ISR）机制，通过自适应调整每个样本的损失权重来优先处理困难样本。在六个大规模结肠镜数据集上的大量实验表明，我们的模型相较于现有最先进检测器具有显著优越性。