An Improved U-Net for Diabetic Retinopathy Segmentation
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-33658-4_12
Reference15 articles.
1. Andersen, J.K.H., Hubel, M.S., Rasmussen, M.L., et al.: Automatic detection of abnormalities and grading of diabetic retinopathy in 6-field retinal images: integration of segmentation into classification. Transl. Vis. Sci. Technol. 11(6), 19–19 (2022)
2. Busbee, B.: The 25-year incidence of visual impairment in type 1 diabetes mellitus: the wisconsin epidemiologic study of diabetic retinopathy. Evid.-Based Ophthalmol. 12(1), 28–29 (2011)
3. Dai, L., Fang, R., Li, H., et al.: Clinical report guided retinal microaneurysm detection with multi-sieving deep learning. IEEE Trans. Med. Imaging 37(5), 1149–1161 (2018)
4. Communications in Computer and Information Science;M Badar,2018
5. Kou, C., Li, W., Liang, W., et al.: Microaneurysms segmentation with a U-Net based on recurrent residual convolutional neural network. J. Med. Imaging 6(2), 025008 (2019)
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