@inproceedings{e953d9787a4742ab9794977adfc0b43f,
title = "SC-GAN: Structure Consistent GAN for Modality Transfer with FFT and Multi-Scale Perception",
abstract = "The quality of the cornea endothelial microscopy image is critical for clinical analysis. Although the noncontact specular microscope is more user-friendly than the contact confocal microscope, the imaging quality of the specular microscope is lower. The modality transfer is a promising solution for image quality enhancement. This paper proposes a Structure Consistent Generative Adversarial Network (SC-GAN) to transfer the imaging style from the specular microscope to the confocal microscope. Specifically, we use the Fourier frequency domain consistency to preserve cell structure and propose a multi-scale perception discriminator to improve model robustness under cell size variation. Experiment results prove the effectiveness of our method.",
keywords = "Corneal Endothelial Cell, Generative Adversarial Network, Modality transfer, Structure Consistency",
author = "Ruiling Xi and Yinglin Zhang and Ruibin Bai and Risa Higashita and Jiang Liu",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 ; Conference date: 18-04-2023 Through 21-04-2023",
year = "2023",
doi = "10.1109/ISBI53787.2023.10230436",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
booktitle = "2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023",
address = "United States",
}