The scarcity of accessible medical image data poses a significant obstacle in effectively training deep learning models for medical diagnosis, as hospitals refrain from sharing their data due to privacy concerns. In response, we gathered a diverse dataset named MedImgs, which comprises over 250,127 images spanning 61 disease types and 159 classes of both humans and animals from open-source repositories. We propose a Leapfrog Latent Consistency Model (LLCM) that is distilled from a retrained diffusion model based on the collected MedImgs dataset, which enables our model to generate real-time high-resolution images. We formulate the reverse diffusion process as a probability flow ordinary differential equation (PF-ODE) and solve it in latent space using the Leapfrog algorithm. This formulation enables rapid sampling without necessitating additional iterations. Our model demonstrates state-of-the-art performance in generating medical images. Furthermore, our model can be fine-tuned with any custom medical image datasets, facilitating the generation of a vast array of images. Our experimental results outperform those of existing models on unseen dog cardiac X-ray images. Source code is available at https://github.com/lskdsjy/LeapfrogLCM.

本研究针对医疗图像数据稀缺的问题，提出了一种新的跳跃潜变量一致性模型（LLCM），该模型通过使用多样化的MedImgs数据集进行训练。其主要创新在于通过概率流常微分方程（PF-ODE）形式化逆扩散过程，从而实现实时高分辨率的医疗图像生成，且在无监管的狗心脏X光图像生成方面显著优于现有模型。

医疗图像生成的跳跃潜变量一致性模型 (LLCM)