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

• URL: http://arxiv.org/abs/2405.09806v4
• Date: Mon, 10 Feb 2025 20:00:24 GMT
• Title: MediSyn: A Generalist Text-Guided Latent Diffusion Model For Diverse Medical Image Synthesis
• Authors: Joseph Cho, Mrudang Mathur, Cyril Zakka, Dhamanpreet Kaur, Matthew Leipzig, Alex Dalal, Aravind Krishnan, Eubee Koo, Karen Wai, Cindy S. Zhao, Rohan Shad, Robyn Fong, Ross Wightman, Akshay Chaudhari, William Hiesinger,
• Abstract summary: MediSyn is a text-guided latent diffusion model capable of generating synthetic images from 6 medical specialties and 10 image types. A direct comparison of the synthetic images against the real images confirms that our model synthesizes novel images and, crucially, may preserve patient privacy. Our findings highlight the immense potential for generalist image generative models to accelerate algorithmic research and development in medicine.
• Score: 4.541407789437896
• License:
• Abstract: Deep learning algorithms require extensive data to achieve robust performance. However, data availability is often restricted in the medical domain due to patient privacy concerns. Synthetic data presents a possible solution to these challenges. Recently, image generative models have found increasing use for medical applications but are often designed for singular medical specialties and imaging modalities, thus limiting their broader utility. To address this, we introduce MediSyn: a text-guided, latent diffusion model capable of generating synthetic images from 6 medical specialties and 10 image types. The synthetic images are validated by expert clinicians for alignment with their corresponding text prompts. Furthermore, a direct comparison of the synthetic images against the real images confirms that our model synthesizes novel images and, crucially, may preserve patient privacy. Finally, classifiers trained on a mixture of synthetic and real data achieve similar performance to those trained on twice the amount of real data. Our findings highlight the immense potential for generalist image generative models to accelerate algorithmic research and development in medicine.

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