Leapfrog Latent Consistency Model (LLCM) for Medical Images Generation

• URL: http://arxiv.org/abs/2411.15084v1
• Date: Fri, 22 Nov 2024 17:19:58 GMT
• Title: Leapfrog Latent Consistency Model (LLCM) for Medical Images Generation
• Authors: Lakshmikar R. Polamreddy, Kalyan Roy, Sheng-Han Yueh, Deepshikha Mahato, Shilpa Kuppili, Jialu Li, Youshan Zhang,
• Abstract summary: We propose a Leapfrog Latent Consistency Model (LLCM) that is distilled from a retrained diffusion model based on the collected MedImgs dataset. Our model demonstrates state-of-the-art performance in generating medical images. Our experimental results outperform those of existing models on unseen dog cardiac X-ray images.
• Score: 11.61653347709148
• License:
• Abstract: 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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