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Abstract #94721 Published in IGR 22-2
Glaucoma screening using an attention-guided stereo ensemble network
Liu Y; Yip LWL; Zheng Y; Wang LMethods 2022; 202: 14-21
Glaucoma is a chronic eye disease, which causes gradual vision loss and eventually blindness. Accurate glaucoma screening at early stage is critical to mitigate its aggravation. Extracting high-quality features are critical in training of classification models. In this paper, we propose a deep ensemble network with attention mechanism that detects glaucoma using optic nerve head stereo images. The network consists of two main sub-components, a deep Convolutional Neural Network that obtains global information and an Attention-Guided Network that localizes optic disc while maintaining beneficial information from other image regions. Both images in a stereo pair are fed into these sub-components, the outputs are fused together to generate the final prediction result. Abundant image features from different views and regions are being extracted, providing compensation when one of the stereo images is of poor quality. The attention-based localization method is trained in a weakly-supervised manner and only image-level annotation is required, which avoids expensive segmentation labelling. RESULTS: from real patient images show that our approach increases recall (sensitivity) from the state-of-the-art 88.89% to 95.48%, while maintaining precision and performance stability. The marked reduction in false-negative rate can significantly enhance the chance of successful early diagnosis of glaucoma.
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Electronic address: yliu050@e.ntu.edu.sg.
Full articleClassification:
1.6 Prevention and screening (Part of: 1 General aspects)
