Paper Info
Title
Attention Based Glaucoma Detection: A Large-Scale Database And Cnn Model
Abstract
Recently, the attention mechanism has been successfully applied in convolutional neural networks (CNNs), significantly boosting the performance of many computer vision tasks. Unfortunately, few medical image recognition approaches incorporate the attention mechanism in the CNNs. In particular, there exists high redundancy in fundus images for glaucoma detection, such that the attention mechanism has potential in improving the performance of CNN-based glaucoma detection. This paper proposes an attention-based CNN for glaucoma detection (AG-CNN). Specifically, we first establish a large-scale attention based glaucoma (LAG) database, which includes 5,824 fundus images labeled with either positive glaucoma (2,392) or negative glaucoma (3,432). The attention maps of the ophthalmologists are also collected in LAG database through a simulated eye-tracking experiment. Then, a new structure of AG-CNN is designed, including an attention prediction sub-net, a pathological area localization subnet and a glaucoma classification subnet. Different from other attention-based CNN methods, the features are also visualized as the localized pathological area, which can advance the performance of glaucoma detection. Finally, the experiment results show that the proposed AG-CNN approach significantly advances state-of-the-art glaucoma detection.
Year
DOI
Venue
2019
10.1109/CVPR.2019.01082
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
Volume
Glaucoma,Pattern recognition,Computer science,Convolutional neural network,Fundus (eye),Subnet,Redundancy (engineering),Artificial intelligence,Boosting (machine learning),Database
Journal
abs/1903.10831
ISSN
Citations 
PageRank 
1063-6919
4
0.36
References 
Authors
0
5
Name
Order
Citations
PageRank
Liu Li181.42
Mai Xu250957.90
Xiaofei Wang350.71
Lai Jiang4377.31
Hanruo Liu5152.59