MediSyn: Text-Guided Diffusion Models for Broad Medical 2D and 3D Image Synthesis

• URL: http://arxiv.org/abs/2405.09806v2
• Date: Wed, 10 Jul 2024 04:04:06 GMT
• Title: MediSyn: Text-Guided Diffusion Models for Broad Medical 2D and 3D Image Synthesis
• Authors: Joseph Cho, Cyril Zakka, Dhamanpreet Kaur, Rohan Shad, Ross Wightman, Akshay Chaudhari, William Hiesinger,
• Abstract summary: In medicine, this application promises to address the critical challenge of data scarcity. By generating realistic and varying medical 2D and 3D images, these models offer a rich, privacy-respecting resource for algorithmic training and research. We show significant improvement in broad medical image and video synthesis guided by text prompts.
• Score: 5.4476703187184246
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion models have recently gained significant traction due to their ability to generate high-fidelity and diverse images and videos conditioned on text prompts. In medicine, this application promises to address the critical challenge of data scarcity, a consequence of barriers in data sharing, stringent patient privacy regulations, and disparities in patient population and demographics. By generating realistic and varying medical 2D and 3D images, these models offer a rich, privacy-respecting resource for algorithmic training and research. To this end, we introduce MediSyn, a pair of instruction-tuned text-guided latent diffusion models with the ability to generate high-fidelity and diverse medical 2D and 3D images across specialties and modalities. Through established metrics, we show significant improvement in broad medical image and video synthesis guided by text prompts.

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