Multi-scale Bottleneck Residual Network for Retinal Vessel Segmentation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Health Information Management,Health Informatics,Information Systems,Medicine (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1007/s10916-023-01992-7.pdf
Reference36 articles.
1. Mookiah, M. R. K., Acharya, U. R., Chua, C. K., Lim, C. M., Ng, E. Y. K., and Laude, A., Computer-aided diagnosis of diabetic retinopathy: a review. Computers in Biology and Medicine 43(12): 2136-2155, 2013. https://doi.org/10.1016/j.compbiomed.2013.10.007
2. Irshad, S., and Akram, M. U., Classification of retinal vessels into arteries and veins for detection of hypertensive retinopathy. In: 2014 Cairo International Biomedical Engineering Conference (CIBEC), pp. 133-136: IEEE, 2014. https://doi.org/10.1109/CIBEC.2014.7020937
3. Kim, Y. Y., and Yoo, E., Author response: diagnostic ability of retinal arteriolar diameter measurements in glaucoma. Investigative Ophthalmology & Visual Science 57(4): 2167-2167, 2016. https://doi.org/10.1167/iovs.16-19352
4. Huang, W., Guo, F., and Yan, Y., Retinal vessel segmentation algorithm based on attention mechanism. In: 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), pp. 1-5: IEEE, 2022. https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927960
5. Zhuang, J., LadderNet: multi-path networks based on U-Net for medical image segmentation. arXiv: 1810.07810, 2018. https://doi.org/10.48550/arXiv.1810.07810
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