Advancing Image-Based Endpoint Development through Generative AI and Transfer Learning

Music City Center 

Generative AI (GenAI) is a powerful tool in image and video generation and is popularized using filters in social media. However, the potential of GenAI in healthcare has yet to be fully explored. In this study, we use GenAI (generative models such GANs or diffusion models) to create synthetic facial vitiligo images that can be used for training traditional computer vision models (such as the UNet). We evaluate the fidelity of the synthetic vitiligo images by using them to train a UNet model and then validating the trained model using real vitiligo images. Next, we compare the accuracy of model trained using synthetic images to a model trained using real vitiligo images on the same validation set of real vitiligo images. Finally, we use the trained UNet to generate clinically meaningful measurements of vitiligo lesions. This framework can be generalized to any disease that can be diagnosed through images. A small set of real images with disease can be used as the foundation to generate a much larger set of synthetic images with disease that researches can use to train and improve the accuracy of their computer vision AI in disease quantification.

Generative AI

Transfer Learning