13 March 2024Development and evaluation of a deep learning model for oral soft and hard tissue diagnosis using synthetic image data generated by StyleGAN
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Automatic intraoral imaging-based assessment of oral conditions is important for clinical and consumer-level oral health monitoring. But there is a lack of publicly available intraoral datasets. To address this, we developed a StyleGAN2-based framework to generate synthetic 2D intraoral images. The StyleGAN2 network was trained on 3724 images with a Frechet Inception Distance 12.10. Dental professionals evaluated image quality and determined if images were real or synthetic. Approximately 83.75% of generated images were deemed real. We created a framework that utilizes pseudo-labeling to incorporate the StyleGAN2-synthesized 2D intraoral images into a tooth type classification model. Our experiments demonstrated that the StyleGAN2 synthesized images can effectively augment the training set and improve the performance of the tooth type classification model.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Ananya Jana,Vrinda Jain,Abmael Oliveira,Hrebesh Molly Subhash, andDimitris Metaxas
"Development and evaluation of a deep learning model for oral soft and hard tissue diagnosis using synthetic image data generated by StyleGAN", Proc. SPIE 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, 128570D (13 March 2024); https://doi.org/10.1117/12.3000633
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Ananya Jana, Vrinda Jain, Abmael Oliveira, Hrebesh Molly Subhash, Dimitris Metaxas, "Development and evaluation of a deep learning model for oral soft and hard tissue diagnosis using synthetic image data generated by StyleGAN," Proc. SPIE 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, 128570D (13 March 2024); https://doi.org/10.1117/12.3000633