Abstract
The retinal nerve fiber layer defect (RNFLD) provides early diagnostic evidence for many irreversible disabling or blinding diseases. This paper aims for automated RNFLD detection based on fundus images. Different from previous works that only consider the local contexts, we are the first to propose to detect RNFLD with position guidance, which senses both the physiological position and global dependencies with ease. Our solution consists of a position-consistent data preprocessing, a Position Guided Network, and a weakly supervised learning strategy. In the position-consistent data preprocessing, the optic disc region is evenly divided into several sectors according to the distribution regularity of RNFL. To detect RNFLD in sectors, the proposed Position Guided Network highlights the significant region with a position-aware attention module and captures the global dependencies with a bidirectional GRU module. The dataset about RNFLD suffers from noise labels, which is verified in our created dataset containing 4,335 fundus images. Thus the weakly supervised learning strategy, which jointly optimizes network parameters and label distributions, is proposed to reduce the impact of noise labels. Tested on a clinical dataset of 750 images, our solution achieves outstanding performance, attaining the F1 score of 81.00% that outperforms the baseline by 13.71%.
G. Cheng—This work is supported by the Beijing Natural Science Foundation (No. 4192029, No. 4202033).
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Ding, F., Yang, G., Ding, D., Cheng, G. (2020). Retinal Nerve Fiber Layer Defect Detection with Position Guidance. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_72
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