Abstract
Medical image segmentation suffers from data scarcity, particularly in polyp detection where annotation requires specialized expertise. We present SynDiff, a framework combining text-guided synthetic data generation with efficient diffusion-based segmentation. Our approach employs latent diffusion models to generate clinically realistic synthetic polyps through text-conditioned inpainting, augmenting limited training data with semantically diverse samples. Unlike traditional diffusion methods requiring iterative denoising, we introduce direct latent estimation enabling single-step inference with T x computational speedup. On CVC-ClinicDB, SynDiff achieves 96.0% Dice and 92.9% IoU while maintaining real-time capability suitable for clinical deployment. The framework demonstrates that controlled synthetic augmentation improves segmentation robustness without distribution shift. SynDiff bridges the gap between data-hungry deep learning models and clinical constraints, offering an efficient solution for deployment in resourcelimited medical settings.
Abstract (translated)
医学图像分割面临着数据稀缺的问题，尤其是在息肉检测中，标注需要专门的知识和技能。我们提出了SynDiff框架，结合了文本引导的合成数据生成与高效的扩散基序分割方法。我们的方法利用潜在扩散模型通过基于文本条件的修复技术来生成临床上真实的合成息肉样本，从而用语义多样化的样本来增强有限的训练数据集。 不同于传统的需要迭代去噪处理的扩散方法，我们引入了直接潜在估计的方法，能够在单步推理中实现T x（其中T为传统模型的迭代次数）的加速效果。在CVC-ClinicDB数据集中，SynDiff达到了96.0%的Dice系数和92.9%的IoU值，并且保持了实时处理能力，适用于临床应用部署。 该框架表明，在不产生分布偏移的情况下，受控合成增强可以提高分割模型的鲁棒性。SynDiff弥合了对大量数据有需求的深度学习模型与临床约束之间的差距，为资源有限的医疗环境提供了一种高效的解决方案。
URL
https://arxiv.org/abs/2507.15361
