@inproceedings{bafa43a3ac294437b9c54507a4909262,
title = "Generative Adversarial Networks with Dense Connection for Optical Coherence Tomography Images Denoising",
abstract = "Optical coherence tomography (OCT) is widely used in the diagnosis of ophthalmic diseases. However, OCT is affected by ubiquitous speckle noise which make it difficult to analysis the retinal structures. To efficiently remove the noise as well as preserve clinical detail information contained in the images, we suggest to train a denoise generative adversarial network (DNGAN) jointly with a densely connected convolutional network to estimate clean OCT images from noisy OCT images. A generator convolutional neural network (CNN) with several dense connections, is trained to transform noisy OCT image into clean OCT image. At the same time, an adversarial CNN is trained to improve the denoising performance of the generator. The experimental results demonstrate the superior performance of our network on OCT images.",
keywords = "OCT, denoising, densely connected convolutional network, generative adversarial network",
author = "Aihui Yu and Xiaoming Liu and Xiangkai Wei and Tianyu Fu and Dong Liu",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018 ; Conference date: 13-10-2018 Through 15-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/CISP-BMEI.2018.8633086",
language = "English",
series = "Proceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Wei Li and Qingli Li and Lipo Wang",
booktitle = "Proceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018",
address = "United States",
}