Abstract
Medical image segmentation models struggle with rare abnormalities due to scarce annotated pathological data. We propose DiffAug a novel framework that combines textguided diffusion-based generation with automatic segmentation validation to address this challenge. Our proposed approach uses latent diffusion models conditioned on medical text descriptions and spatial masks to synthesize abnormalities via inpainting on normal images. Generated samples undergo dynamic quality validation through a latentspace segmentation network that ensures accurate localization while enabling single-step inference. The text prompts, derived from medical literature, guide the generation of diverse abnormality types without requiring manual annotation. Our validation mechanism filters synthetic samples based on spatial accuracy, maintaining quality while operating efficiently through direct latent estimation. Evaluated on three medical imaging benchmarks (CVC-ClinicDB, Kvasir-SEG, REFUGE2), our framework achieves state-of-the-art performance with 8-10% Dice improvements over baselines and reduces false negative rates by up to 28% for challenging cases like small polyps and flat lesions critical for early detection in screening applications.
Abstract (translated)
医学图像分割模型在处理罕见异常情况时，由于缺乏标注的病理数据而面临挑战。为此，我们提出了一种名为DiffAug的新框架，该框架结合了基于文本引导的扩散生成技术和自动分割验证技术来应对这一难题。我们的方法利用条件于医疗文本描述和空间掩码的潜在扩散模型，在正常图像上通过修补（inpainting）生成异常情况。生成的数据会经过一个在潜在空间中进行分割网络的动态质量验证过程，确保准确的定位同时支持一步推理操作。从医学文献中提取的文本提示引导生成不同类型且多样化的异常情况，无需手动注释。我们的验证机制根据空间准确性过滤合成样本，在保持高质量的同时通过直接潜在估计实现高效运行。 我们在三个医疗成像基准数据集（CVC-ClinicDB、Kvasir-SEG 和 REFUGE2）上评估了该框架，并取得了与现有方法相比在Dice系数上有8%-10%的性能提升，尤其是在小型息肉和扁平病变等挑战性案例中减少了高达28%的假阴性率。这些类型的异常对于筛查应用中的早期检测至关重要。
URL
https://arxiv.org/abs/2508.17844
