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Panoramic x-ray dataset augmentation using generative AI
Deep learning has proven to be an effective method to improve condition identification in healthcare using x-ray images [1]. However, developing deep learning models can be difficult because accessing appropriate training datasets can be complicated due to high costs of expert labeling or privacy restrictions due sensitive patient information [2]. Generative AI has the potential to overcome these limitations by producing synthetic medical images that closely resemble real patient data [3]. In this post I review some generative AI models that have been used to generate images and explain how one of them, diffusion models, can augment an x-ray dataset using Medical Open Network for Artificial Intelligence (MONAI) platform.
What are generative AI models?
Generative AI models are a category of artificial intelligence models that are designed to generate new data samples, such as images, text, audio, or other types of content, that are similar to or indistinguishable from data samples in a given dataset. Generative AI models are typically trained on large datasets to learn patterns, structures, and features present in the data, enabling them to create new, original data samples that resemble the training data. Some examples of generative AI models applied to images are:
