Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras and plays an important role in the prevention and treatment of colorectal cancer in computer-aided diagnosis. However, traditional methods for object ReID directly adopting CNN models trained on the ImageNet dataset usually produce unsatisfactory retrieval performance on colonoscopic datasets due to the large domain gap. Additionally, these methods neglect to explore the potential of self-discrepancy among intra-class relations in the colonoscopic polyp dataset, which remains an open research problem in the medical community. To solve this dilemma, we propose a simple but effective training method named Colo-ReID, which can help our model to learn more general and discriminative knowledge based on the meta-learning strategy in scenarios with fewer samples. Based on this, a dynamic Meta-Learning Regulation mechanism called MLR is introduced to further boost the performance of polyp re-identification. To the best of our knowledge, this is the first attempt to leverage the meta-learning paradigm instead of traditional machine learning to effectively train deep models in the task of colonoscopic polyp re-identification. Empirical results show that our method significantly outperforms current state-of-the-art methods by a clear margin.

结直肠镜息肉重新识别方法通过引入元学习策略，极大提高了模型在大型胶片库中的同一息肉匹配准确率，并且在解决学科内广泛存在的自身差异问题上取得明显优势。

基于元学习的结肠镜息肉重新识别的判别性表示