用扩散模型生成逼真乳腺超声图像，助力医疗数据匮乏问题
Ultrasound Image Generation using Latent Diffusion Models
- 在公开数据集上微调大模型，实现从简单提示生成高质量超声图
- 三名专家评估认为生成图像真实可信，支持病理特征表达
- 通过控制网络实现分割图引导生成，适合医学影像研究者使用
扩散模型在图像生成方面展现出生成多样且高质量图像的潜力。在医学影像领域，由于开源医学图像获取困难，尤其是罕见病图像，生成图像可用来训练分类与分割模型。本文提出通过在多个公开数据库上逐步微调大型扩散模型，模拟真实超声（US）图像。我们基于乳腺超声图像数据集BUSI，对当前先进的潜在扩散模型Stable Diffusion进行微调。仅需指定器官和病灶的简单提示，即可生成高质量乳腺超声图像，经三位经验丰富的超声科学家及一名超声放射科医生评估，生成图像具有高度真实性。此外，我们通过ControlNet引入分割图作为条件，实现用户对生成结果的精确控制。代码将发布于http://code.sonography.ai/，供科研社区快速生成超声图像。
原文摘要 · Abstract (English)
Diffusion models for image generation have been a subject of increasing interest due to their ability to generate diverse, high-quality images. Image generation has immense potential in medical imaging because open-source medical images are difficult to obtain compared to natural images, especially for rare conditions. The generated images can be used later to train classification and segmentation models. In this paper, we propose simulating realistic ultrasound (US) images by successive fine-tuning of large diffusion models on different publicly available databases. To do so, we fine-tuned Stable Diffusion, a state-of-the-art latent diffusion model, on BUSI (Breast US Images) an ultrasound breast image dataset. We successfully generated high-quality US images of the breast using simple prompts that specify the organ and pathology, which appeared realistic to three experienced US scientists and a US radiologist. Additionally, we provided user control by conditioning the model with segmentations through ControlNet. We will release the source code at http://code.sonography.ai/ to allow fast US image generation to the scientific community.
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