In order to take advantage of AI solutions in endoscopy diagnostics, we must overcome the issue of limited annotations. These limitations are caused by the high privacy concerns in the medical field and the requirement of getting aid from experts for the time-consuming and costly medical data annotation process. In computer vision, image synthesis has made a significant contribution in recent years as a result of the progress of generative adversarial networks (GANs) and diffusion probabilistic models (DPM). Novel DPMs have outperformed GANs in text, image, and video generation tasks. Therefore, this study proposes a conditional DPM framework to generate synthetic GI polyp images conditioned on given generated segmentation masks. Our experimental results show that our system can generate an unlimited number of high-fidelity synthetic polyp images with the corresponding ground truth masks of polyps. To test the usefulness of the generated data, we trained binary image segmentation models to study the effect of using synthetic data. Results show that the best micro-imagewise IOU of 0.7751 was achieved from DeepLabv3+ when the training data consists of both real data and synthetic data. However, the results reflect that achieving good segmentation performance with synthetic data heavily depends on model architectures.

翻译：为在内窥镜诊断中充分利用人工智能解决方案，我们必须克服标注数据有限的问题。这一局限性源于医疗领域对隐私的高度关注，以及需要专家参与耗时且昂贵的医疗数据标注过程。在计算机视觉领域，随着生成对抗网络（GANs）和扩散概率模型（DPM）的发展，图像合成近年来取得了显著进展。新型扩散概率模型在文本、图像和视频生成任务中已超越生成对抗网络。因此，本研究提出了一种条件扩散概率模型框架，用于生成以给定分割掩码为条件的合成胃肠道息肉图像。实验结果表明，我们的系统可生成无限数量的高保真合成息肉图像及其对应的真实掩码。为验证生成数据的实用性，我们训练了二值图像分割模型以研究合成数据的影响。结果显示，当训练数据包含真实数据和合成数据时，DeepLabv3+模型取得了最佳微图像交并比0.7751。但结果也表明，使用合成数据获得良好分割性能高度依赖于模型架构。