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DSLN: Dual-tutor student learning network for multiracial glaucoma detection

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Abstract

Accurate early glaucoma detection is crucial to prevent further vision loss. However, using the off-the-shelf models against fundus image datasets of different races may lead to degraded performance due to domain shift. To address the issue, this paper proposes a dual-tutor student learning network (DSLN) for multiracial glaucoma detection. The proposed DSLN consists of an inter-image tutor, an intra-image tutor, student model and backbone network, which combines the advantages of domain adaptation and semi-supervised learning. The inter-image tutor uses CycleGAN for style transfer to reduce the appearance differences between labeled source domain and labeled target domain images, and transfers the learned knowledge to the student model by minimizing knowledge distillation loss. The intra-image tutor adopts the exponential moving average to leverage the unlabeled target domain and transfers the knowledge to the student model by minimizing prediction consistency loss. Moreover, the student model not only directly learns knowledge from the labeled target domain images, but also learns the intra-image knowledge and inter-image knowledge transfer by two tutors. Furthermore, the backbone integrates the context features of the local optic disc region and global fundus image via modified ResNet50. We conduct extensive experiments on three scenarios constructed from nine public fundus image datasets of three races. Comprehensive experimental results show that the proposed DSLN framework outperforms the state-of-the-art models and has good robustness and generalization: it can effectively overcome domain shift and accurately detect glaucoma from multi-ethnic fundus images.

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Availability of data and materials

Data related to the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors gratefully acknowledge the financial supports by the National Natural Science Foundation of China (Grant No. 61976126), Shandong Nature Science Foundation of China (Grant No. ZR2019MF003, ZR2020MF132, ZR2020MH291).

Funding

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61976126), Shandong Nature Science Foundation of China (Grant No. ZR2019MF003, ZR2020MF132, ZR2020MH291).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China

    Yanfei Guo, Yanjun Peng, Jindong Sun, Dapeng Li & Bin Zhang

  2. Shandong Province Key Laboratory of Wisdom Mining Information Technology, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China

    Yanjun Peng

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  1. Yanfei Guo
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Contributions

Yanfei Guo contributed to conceptualization, methodology, software, writing the original draft. Yanjun Peng helped in data curation, supervision. Jindong Sun contributed to investigation, formal analysis, software. Dapeng Li contributed to visualization, writing—review and editing. Bin Zhang contributed to resources, project administration.

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Correspondence to Yanjun Peng.

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Guo, Y., Peng, Y., Sun, J. et al. DSLN: Dual-tutor student learning network for multiracial glaucoma detection. Neural Comput & Applic 34, 11885–11910 (2022). https://doi.org/10.1007/s00521-022-07078-8

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