Image synthesis approaches, e.g., generative adversarial networks, have been popular as a form of data augmentation in medical image analysis tasks. It is primarily beneficial to overcome the shortage of publicly accessible data and associated quality annotations. However, the current techniques often lack control over the detailed contents in generated images, e.g., the type of disease patterns, the location of lesions, and attributes of the diagnosis. In this work, we adapt the latest advance in the generative model, i.e., the diffusion model, with the added control flow using lesion-specific visual and textual prompts for generating dermatoscopic images. We further demonstrate the advantage of our diffusion model-based framework over the classical generation models in both the image quality and boosting the segmentation performance on skin lesions. It can achieve a 9% increase in the SSIM image quality measure and an over 5% increase in Dice coefficients over the prior arts.

翻译：图像合成方法（例如生成对抗网络）在医学图像分析任务中作为数据增强手段已得到广泛应用。该方法主要有助于克服公开数据及其高质量标注的短缺问题。然而，现有技术往往缺乏对生成图像中细节内容的控制能力，例如病灶类型模式、病变部位及诊断属性。本研究采用生成模型的最新进展——扩散模型，通过引入基于病变特异性视觉和文本提示的控制流，实现皮肤镜图像的生成。我们进一步证明了基于扩散模型的框架在图像质量及皮肤病变分割性能提升方面均优于经典生成模型。与现有最优方法相比，该框架在SSIM图像质量指标上提升了9%，Dice系数提升了5%以上。