Abstract
Exudate detection in fundus images is an important task for the screening of people with diabetic retinopathy. In this paper, Convolutional Neural Network (CNN) is used to detect the exudates in fundus images. An auxiliary loss for classification is designed to better train the CNN architecture. Besides, we use a boosted training method to improve and speed-up the CNN training. The trained model has been evaluated on our own annotated dataset and three public available databases, obtaining an AUC of 0.98, 0.96, 0.94, 0.91 respectively.
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Acknowledgment
This work was supported in part by National Natural Science Foundation of China (61671289, 61221001, 61771303).
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Li, G., Zheng, S., Li, X. (2018). Exudate Detection in Fundus Images via Convolutional Neural Network. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_18
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DOI: https://doi.org/10.1007/978-981-10-8108-8_18
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