RoentGen: Vision-Language Foundation Model for Chest X-ray Generation

Multimodal models trained on large natural image-text pair datasets have exhibited astounding abilities in gener-ating high-quality images. Medical imaging data is fundamentally different to natural images, and the language used to succinctly capture relevant details in medical data uses a different, narrow but semantically rich, domain-specific vocabulary.

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Pierre Chambon, Christian Bluethgen, Jean-Benoit Delbrouck, Rogier Van der Sluijs, Małgorzata Połacin, Juan Manuel Zambrano Chaves, Tanishq Mathew Abraham, Shivanshu Purohit, Curtis P. Langlotz, Akshay Chaudhari (2024). Dataset: RoentGen: Vision-Language Foundation Model for Chest X-ray Generation. https://doi.org/10.57702/4gsh3dkb

DOI retrieved: December 2, 2024

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Created December 2, 2024
Last update December 2, 2024
Author Pierre Chambon
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Christian Bluethgen
Jean-Benoit Delbrouck
Rogier Van der Sluijs
Małgorzata Połacin
Juan Manuel Zambrano Chaves
Tanishq Mathew Abraham
Shivanshu Purohit
Curtis P. Langlotz
Akshay Chaudhari
Homepage https://arxiv.org/abs/2210.04133