Attention Based Glaucoma Detection: A Large-scale Database and CNN Model

TL;DR

This paper introduces AG-CNN, an attention-based convolutional neural network for glaucoma detection, leveraging a large-scale, annotated fundus image database and attention maps to improve accuracy and interpretability.

Contribution

The paper presents a novel AG-CNN model with a new structure and a large-scale glaucoma database, integrating ophthalmologist attention maps for enhanced detection performance.

Findings

AG-CNN significantly outperforms existing methods.

The large-scale LAG database supports robust model training.

Attention maps improve interpretability of glaucoma detection.

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 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.