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.

翻译：结肠镜息肉再识别旨在从不同视角、不同相机拍摄的大规模图像库中匹配同一息肉，在计算机辅助诊断的结直肠癌预防和治疗中发挥重要作用。然而，传统对象再识别方法直接采用在ImageNet数据集上训练的CNN模型，由于巨大的域差异，通常无法在结肠镜数据集上获得满意的检索性能。此外，这些方法忽视了探索结肠镜息肉数据集中类内关系间的自差异性潜力，这在医学界仍是一个开放性研究问题。为解决这一困境，我们提出一种简单而有效的训练方法Colo-ReID，该方法可在样本较少场景下，基于元学习策略帮助模型学习更具泛化性和判别性的知识。在此基础上，引入一种称为MLR的动态元学习调控机制，进一步提升息肉再识别的性能。据我们所知，这是首次尝试利用元学习范式而非传统机器学习，有效训练深度模型以完成结肠镜息肉再识别任务。实验结果表明，我们的方法显著优于当前最先进方法，具有明显优势。