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Biological imaging studies are often limited by a low amount of data, decreasing the reliability of typical machine learning methods. We attempted to address this by creating large numbers of synthetic second harmonic generation images that can be tuned to reflect properties of different disease classes. To start, we collected collagen images from a variety of healthy and diseased specimens. These were analyzed with a modified generative adversarial network (StyleGAN) combined with an encoder. After training, we were able to produce images that accurately reflected our samples. These results can be applied to increase the accuracy of classification algorithms and models of extracellular matrix tissue.
Melissa Champer,Vikas Singh, andPaul Campagnola
"Artificial image tuning using a generative adversarial network on collagen images from second harmonic generation microscopy", Proc. SPIE PC12363, Multiscale Imaging and Spectroscopy IV, PC1236309 (17 March 2023); https://doi.org/10.1117/12.2649436
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Melissa Champer, Vikas Singh, Paul Campagnola, "Artificial image tuning using a generative adversarial network on collagen images from second harmonic generation microscopy," Proc. SPIE PC12363, Multiscale Imaging and Spectroscopy IV, PC1236309 (17 March 2023); https://doi.org/10.1117/12.2649436