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Stanford's AI Model based on Stable Diffusion

Updated: Jan 31

Latent diffusion models are a powerful tool for a wide range of tasks in computer vision. They are able to capture complex patterns in the data and are flexible when it comes to new domain adaptation. Their ability to create high-fidelity and diverse images is important because obtaining real-world data for training deep learning models is frequently scarce. As a result, engineers often resort to using synthetic data for training and/or fine-tuning deep learning networks.

RoentGen’s potential of using a small fine-tuning dataset to increase the representation capability of certain diseases with conditioned latent diffusion models is far-reaching and could benefit the whole medical AI community in the future.

See an interesting review by Aneesh Tickoo (Dec 2, 2022) on the Mark Tech Post here.

Thanks to Levente Göncz for his input!


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