  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 subnet, 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.
