  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.
