用25万张医学影像训练出实时生成高分辨率图像的模型
Leapfrog Latent Consistency Model (LLCM) for Medical Images Generation
- 基于扩散模型重构，用跳跃算法在隐空间快速采样
- 生成速度极快，真实医疗影像生成质量达顶尖水平
- 支持微调，适合医疗数据稀缺场景下的图像生成
医疗影像数据因隐私问题难以获取，制约了深度学习模型的训练。为此，我们构建了一个名为MedImgs的多样化数据集，包含超过250,127张图像，涵盖61种疾病类型和159类人与动物的影像，数据来源于开源资源。我们提出一种从重训练扩散模型中蒸馏得到的跃迁隐空间一致性模型（Leapfrog Latent Consistency Model, LLCM），可在隐空间中通过跃迁算法求解概率流常微分方程（PF-ODE），实现无需额外迭代的快速采样，从而实现实时生成高分辨率医学图像。该模型在生成性能上达到当前最优水平，并可针对任意自定义医疗影像数据集进行微调，显著扩展生成能力。实验表明，其在未见过的狗心脏X光片生成任务中优于现有模型。代码已公开于https://github.com/lskdsjy/LeapfrogLCM。
原文摘要 · Abstract (English)
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.
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