Latent Diffusion Models: Generating High-Fidelity Medical Images with Text Conditioning

Latent Diffusion Models (LDMs) have become popular in the field of AI because they allow for the generation of high-quality images with fine control over the production process. These models are particularly useful when combined with conditioning mechanisms and can produce images based on text prompts. To train these models, large datasets like LAION5B are often used. LDMs have the advantage of operating in a low-dimensional space and requiring minimal hardware resources, making them easier to deploy to end users.

One of the challenges in the field of medical imaging is the lack of carefully curated datasets. However, by using text-based radiology reports, it is possible to extract labels automatically for downstream activities. With the use of pre-trained text conditional LDMs, it is now possible to synthesize medical imaging data by prompting relevant medical terms or concepts.

This study explores the adaptation of a vision-language LDM to medical imaging, specifically for producing chest X-rays. Chest X-rays are a commonly used imaging modality that provides valuable information on various medical conditions. The researchers assessed the representative capacity of the LDM pipeline and explored different methods for enhancing the model’s ability to represent medical concepts specific to chest X-rays. They developed a generative model called RoentGen, which can synthesize high-fidelity chest X-rays based on text prompts.

The study also presents a framework for evaluating the correctness of domain-adapted text-to-image models using specific tasks related to medical imaging. The researchers found that fine-tuning the text encoders improved image fidelity and conceptual correctness. They also discovered that the text encoder’s ability to express medical concepts is enhanced when trained along with the U-Net. RoentGen can be fine-tuned on a small subset of images and used to supplement data for image classification tasks.

To learn more about this research, you can check out the paper and project. Join our Reddit page and Discord channel for the latest AI research news and interesting projects. Aneesh Tickoo is a consulting intern at MarktechPost and is currently pursuing his undergraduate degree in Data Science and Artificial Intelligence. He is passionate about machine learning and image processing and loves collaborating on exciting projects.

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