本文介绍了一种在结肠镜检查图像中自动进行息肉分割的创新方法，该方法部署了增强型 Pix2Pix 生成对抗网络（GAN），并在鉴别器中配备了集成注意机制。我们的模型解决了传统分割方法中普遍存在的挑战，例如可变的息肉外观、不一致的图像质量和有限的训练数据，显着提高了息肉分割的精度和可靠性。注意力机制的集成使我们的模型能够细致地关注息肉的复杂特征，从而提高分割准确性。采用独特的训练策略，采用真实数据和合成数据，以确保模型在各种条件下的稳健性能。通过对多个公共结肠镜检查数据集进行严格测试验证的结果表明，与现有最先进的方法相比，分割性能有了显着提高。我们的模型增强了早期检测关键细节的能力，在主动结直肠癌检测（智能医疗系统的一个关键方面）中发挥着关键作用。这项工作代表了先进人工智能技术和医疗物联网 (IoMT) 的有效融合，标志着对智能医疗系统发展的显着贡献。总之，我们的注意力增强型 Pix2Pix GAN 不仅提供高效可靠的息肉分割，而且还展示了无缝集成到远程健康监测系统的巨大潜力，突显了人工智能在推进 IoMT 医疗保健方面日益增强的相关性和有效性。

This paper introduces an innovative approach for automated polyp segmentation in colonoscopy images, deploying an enhanced Pix2Pix Generative Adversarial Network (GAN) equipped with an integrated attention mechanism in the discriminator. Addressing prevalent challenges in conventional segmentation methods, such as variable polyp appearances, inconsistent image quality, and limited training data, our model significantly augments the precision and reliability of polyp segmentation. The integration of an attention mechanism enables our model to meticulously focus on the intricate features of polyps, improving segmentation accuracy. A unique training strategy, employing both real and synthetic data, is adopted to ensure the model's robust performance under a variety of conditions. The results, validated through rigorous tests on multiple public colonoscopy datasets, indicate a notable improvement in segmentation performance over existing state-of-the-art methods. Our model's enhanced ability to detect critical details early plays a pivotal role in proactive colorectal cancer detection, a key aspect of smart healthcare systems. This work represents an effective amalgamation of advanced AI techniques and the Internet of Medical Things (IoMT), signifying a noteworthy contribution to the evolution of smart healthcare systems. In conclusion, our attention-enhanced Pix2Pix GAN not only offers efficient and reliable polyp segmentation, but also showcases considerable potential for seamless integration into remote health monitoring systems, underlining the increasing relevance and efficacy of AI in advancing IoMT-enabled healthcare.