TY - GEN
T1 - Data-driven enhancement of blurry retinal images via generative adversarial networks
AU - Zhao, He
AU - Yang, Bingyu
AU - Cao, Lvchen
AU - Li, Huiqi
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - In this paper, we aim at improving the quality of blurry retinal images that are caused by ocular diseases. The blurry images could affect clinical diagnosis for both ophthalmologists and automatic aided system. Inspired by the great success of generative adversarial networks, a data-driven approach is proposed to enhance the blurry images in a weakly supervised manner. That is to say, instead of paired blurry and high-quality images, our approach can be trained with two sets of unpaired images. The advantage of unpaired training setting makes our approach easily applicable, since the annotated data are very limited in medical images. Compared with traditional methods, our model is an end-to-end approach without human designed adjustments or prior knowledge. However, it achieves a superior performance on blurry images. Besides, a dynamic retinal image feature constraint is proposed to guide the generator to improve the performance and avoid over-enhancing the extremely blurry region. Our approach can work on large image resolution which makes it widely beneficial to clinic images.
AB - In this paper, we aim at improving the quality of blurry retinal images that are caused by ocular diseases. The blurry images could affect clinical diagnosis for both ophthalmologists and automatic aided system. Inspired by the great success of generative adversarial networks, a data-driven approach is proposed to enhance the blurry images in a weakly supervised manner. That is to say, instead of paired blurry and high-quality images, our approach can be trained with two sets of unpaired images. The advantage of unpaired training setting makes our approach easily applicable, since the annotated data are very limited in medical images. Compared with traditional methods, our model is an end-to-end approach without human designed adjustments or prior knowledge. However, it achieves a superior performance on blurry images. Besides, a dynamic retinal image feature constraint is proposed to guide the generator to improve the performance and avoid over-enhancing the extremely blurry region. Our approach can work on large image resolution which makes it widely beneficial to clinic images.
UR - http://www.scopus.com/inward/record.url?scp=85075631600&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32239-7_9
DO - 10.1007/978-3-030-32239-7_9
M3 - Conference contribution
AN - SCOPUS:85075631600
SN - 9783030322380
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 75
EP - 83
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -
