A Classification Method for Diabetic Retinopathy Based on Self-supervised Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5689-6_30
Reference19 articles.
1. Tsiknakis, N., et al.: Deep learning for diabetic retinopathy detection and classification based on fundus images: a review. Comput. Biol. Med. 135, 104599 (2021). https://doi.org/10.1016/J.COMPBIOMED.2021.104599
2. Ting, D.S.W., et al.: Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318, 2211–2223 (2017). https://doi.org/10.1001/JAMA.2017.18152
3. Zhu, J., Li, Y., Hu, Y., Ma, K., Zhou, S.K., Zheng, Y.: Rubik’s Cube+: a self-supervised feature learning framework for 3D medical image analysis. Med. Image Anal. 64, 101746 (2020). https://doi.org/10.1016/J.MEDIA.2020.101746
4. Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.: Self-supervised learning for medical image analysis using image context restoration. Med. Image Anal. 58, 101539 (2019). https://doi.org/10.1016/J.MEDIA.2019.101539
5. Vives-Boix, V., Ruiz-Fernández, D.: Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity. Comput. Methods Programs Biomed. 206, 106094 (2021). https://doi.org/10.1016/j.cmpb.2021.106094
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