早期发现息肉对于降低结直肠癌 (CRC) 发病率至关重要。因此，开发高效、准确的息肉分割技术对于临床CRC预防至关重要。在本文中，我们提出了一种采用扩散模型的息肉分割的端到端训练方法。图像被视为先验，分割被表述为掩模生成过程。在采样过程中，使用训练后的模型为每个输入图像生成多个预测，并通过使用多数投票策略实现显着的性能增强。使用四个公共数据集和一个内部数据集来训练和测试模型性能。该方法在数据集 Kvasir-SEG 和 CVC-ClinicDB 上分别获得了 0.934 和 0.967 的 mDice 分数。此外，应用一种交叉验证来测试所提出模型的泛化性，据我们所知，所提出的方法优于以前的最先进（SOTA）模型。所提出的方法还显着提高了分割精度并具有很强的泛化能力。版权所有 © 2024 Elsevier Ltd. 保留所有权利。

Early detection of polyps is essential to decrease colorectal cancer(CRC) incidence. Therefore, developing an efficient and accurate polyp segmentation technique is crucial for clinical CRC prevention. In this paper, we propose an end-to-end training approach for polyp segmentation that employs diffusion model. The images are considered as priors, and the segmentation is formulated as a mask generation process. In the sampling process, multiple predictions are generated for each input image using the trained model, and significant performance enhancements are achieved through the use of majority vote strategy. Four public datasets and one in-house dataset are used to train and test the model performance. The proposed method achieves mDice scores of 0.934 and 0.967 for datasets Kvasir-SEG and CVC-ClinicDB respectively. Furthermore, one cross-validation is applied to test the generalization of the proposed model, and the proposed methods outperformed previous state-of-the-art(SOTA) models to the best of our knowledge. The proposed method also significantly improves the segmentation accuracy and has strong generalization capability.Copyright © 2024 Elsevier Ltd. All rights reserved.