Abstract
Retinal age has recently emerged as a reliable ageing biomarker for assessing risks of ageing-related diseases. Several studies propose to train deep learning models to estimate retinal age from fundus images. However, the limitation of these studies lies in 1) both of them only train models on snapshot images from single cohorts; 2) they ignore label ambiguity and individual variance in the modeling part. In this study, we propose a progressive label distribution learning (LDL) method with temporal fundus images to improve the retinal age estimation on snapshot fundus images from multiple cohorts. First, we design a two-stage LDL regression head to estimate adaptive age distribution for individual images. Then, we eliminate cohort variance by introducing ordinal constraints to align image features from different data sources. Finally, we add a temporal branch to model sequential fundus images and use the captured temporal evolution as auxiliary knowledge to enhance the model’s predictive performance on snapshot fundus images. We use a large retinal fundus image dataset which consists of \(\sim \)130k images from multiple cohorts to verify our method. Extensive experiments provide evidence that our model can achieve lower age prediction errors than existing methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
We omit the i in feature notions for simplicity.
- 2.
References
Chen, S., Zhang, C., Dong, M., Le, J., Rao, M.: Using ranking-CNN for age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5183–5192 (2017)
Cheng, X., et al.: Population ageing and mortality during 1990–2017: a global decomposition analysis. PLoS Med. 17(6), e1003138 (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Horvath, S., Raj, K.: DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19(6), 371–384 (2018)
Hu, W., et al.: Retinal age gap as a predictive biomarker of future risk of Parkinson’s disease. Age and Ageing 51(3), afac062 (2022)
Lee, J., et al.: Deep learning-based brain age prediction in normal aging and dementia. Nature Aging 2(5), 412–424 (2022)
Li, Q., et al.: Unimodal-concentrated loss: Fully adaptive label distribution learning for ordinal regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20513–20522 (2022)
Li, W., Huang, X., Lu, J., Feng, J., Zhou, J.: Learning probabilistic ordinal embeddings for uncertainty-aware regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13896–13905 (2021)
Liu, C., et al.: Biological age estimated from retinal imaging: a novel biomarker of aging. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 138–146. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_16
Lowsky, D.J., Olshansky, S.J., Bhattacharya, J., Goldman, D.P.: Heterogeneity in healthy aging. J. Gerontol. Series A: Biomed. Sci. Med. Sci. 69(6), 640–649 (2014)
Pan, H., Han, H., Shan, S., Chen, X.: Mean-variance loss for deep age estimation from a face. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5285–5294 (2018)
Peretz, L., Rappoport, N.: Deviation of physiological from chronological age is associated with health. In: Challenges of Trustable AI and Added-Value on Health, pp. 224–228. IOS Press (2022)
Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., Jégou, H.: Going deeper with image transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 32–42 (2021)
Zhang, S., Yang, L., Mi, M.B., Zheng, X., Yao, A.: Improving deep regression with ordinal entropy. arXiv preprint arXiv:2301.08915 (2023)
Zhen, X., Meng, Z., Chakraborty, R., Singh, V.: On the versatile uses of partial distance correlation in deep learning. In: Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXVI. pp. 327–346. Springer (2022). https://doi.org/10.1007/978-3-031-19809-0_19
Zhu, Z., et al.: Association of retinal age gap with arterial stiffness and incident cardiovascular disease. Stroke 53(11), 3320–3328 (2022)
Zhu, Z., et al.: Retinal age gap as a predictive biomarker for mortality risk. British J. Ophthalmol. 107(4), 547–554 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yu, Z. et al. (2023). Retinal Age Estimation with Temporal Fundus Images Enhanced Progressive Label Distribution Learning. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_59
Download citation
DOI: https://doi.org/10.1007/978-3-031-43990-2_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43989-6
Online ISBN: 978-3-031-43990-2
eBook Packages: Computer ScienceComputer Science (R0)
