TY - GEN
T1 - Multi-task fundus image quality assessment via transfer learning and landmarks detection
AU - Shen, Yaxin
AU - Fang, Ruogu
AU - Sheng, Bin
AU - Dai, Ling
AU - Li, Huating
AU - Qin, Jing
AU - Wu, Qiang
AU - Jia, Weiping
N1 - Funding Information:
Acknowledgement. This work is partially supported by National Key Research and Development Program of China (No: 2016YFC1300302, 2017YFE0104000) and by National Natural Science Foundation of China (No: 61525106, 61427807).
Funding Information:
This work is partially supported by National Key Research and Development Program of China (No: 2016YFC1300302, 2017YFE0104000) and by National Natural Science Foundation of China (No: 61525106, 61427807).
Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018/9
Y1 - 2018/9
N2 - The quality of fundus images is critical for diabetic retinopathy diagnosis. The evaluation of fundus image quality can be affected by several factors, including image artifact, clarity, and field definition. In this paper, we propose a multi-task deep learning framework for automated assessment of fundus image quality. The network can classify whether an image is gradable, together with interpretable information about quality factors. The proposed method uses images in both rectangular and polar coordinates, and fine-tunes the network from trained model grading of diabetic retinopathy. The detection of optic disk and fovea assists learning the field definition task through coarse-to-fine feature encoding. The experimental results demonstrate that our framework outperform single-task convolutional neural networks and reject ungradable images in automated diabetic retinopathy diagnostic systems.
AB - The quality of fundus images is critical for diabetic retinopathy diagnosis. The evaluation of fundus image quality can be affected by several factors, including image artifact, clarity, and field definition. In this paper, we propose a multi-task deep learning framework for automated assessment of fundus image quality. The network can classify whether an image is gradable, together with interpretable information about quality factors. The proposed method uses images in both rectangular and polar coordinates, and fine-tunes the network from trained model grading of diabetic retinopathy. The detection of optic disk and fovea assists learning the field definition task through coarse-to-fine feature encoding. The experimental results demonstrate that our framework outperform single-task convolutional neural networks and reject ungradable images in automated diabetic retinopathy diagnostic systems.
KW - Fovea detection
KW - Fundus image quality assessment
KW - Multi-task learning
KW - Optic disk detection
UR - https://www.scopus.com/pages/publications/85054520055
U2 - 10.1007/978-3-030-00919-9_4
DO - 10.1007/978-3-030-00919-9_4
M3 - Conference article published in proceeding or book
AN - SCOPUS:85054520055
SN - 9783030009182
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 28
EP - 36
BT - Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings
A2 - Liu, Mingxia
A2 - Suk, Heung-Il
A2 - Shi, Yinghuan
PB - Springer-Verlag
T2 - 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 16 September 2018
ER -
