MediSyn: Text-Guided Diffusion Models for Broad Medical 2D and 3D Image Synthesis
<|reference_start|>MediSyn: Text-Guided Diffusion Models for Broad Medical 2D and 3D Image Synthesis: 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.<|reference_end|>
@article{cho2024medisyn:,
    title={MediSyn: Text-Guided Diffusion Models for Broad Medical 2D and 3D Image
  Synthesis},
    author={Joseph Cho, Cyril Zakka, Dhamanpreet Kaur, Rohan Shad, Ross Wightman,
  Akshay Chaudhari, William Hiesinger},
    journal={arXiv preprint arXiv:2405.09806},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09806},
    primaryClass={cs.CV cs.AI cs.CL cs.LG}
}