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MediSyn: A Generalist Text-Guided Latent Diffusion Model For Diverse Medical Image Synthesis

Created by
  • Haebom

Author

Joseph Cho, Mrudang Mathur, Cyril Zakka, Dhamanpreet Kaur, Matthew Leipzig, Alex Dalal, Aravind Krishnan, Eubee Koo, Karen Wai, Cindy S. Zhao, Akshay Chaudhari, Matthew Duda, Ashley Choi, Ehsan Rahimy, Lyna Azzouz, Robyn Fong, Rohan Shad, William Hiesinger

Outline

To address the lack of medical image data, this paper presents MediSyn, a text-based latent diffusion model that generates synthetic medical images across six medical specialties and ten image types. MediSyn performs equally or better than specialty-specific models, and expert evaluations confirm its realism and high consistency with text prompts. Furthermore, we demonstrate visual discriminability from real patient images and experimentally demonstrate that classifiers trained solely on synthetic data or augmented with real data outperform classifiers trained solely on real data in data-poor environments. This highlights the potential of this general medical image generation model.

Takeaways, Limitations

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Takeaways:
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Presenting an effective approach to addressing the lack of medical imaging data.
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Development of a general synthetic data generation model applicable to various medical specialties and image types.
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Presenting the possibility of accelerating algorithm research and development using synthetic data.
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Demonstrating the utility of synthetic data in data-poor environments.
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Limitations:
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The paper does not explicitly mention the specific Limitations. Future research should further improve the model's generalization performance and expand its applicability to various medical image types. Further validation is needed for practical clinical applications.
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