The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
(2007) · Developments in Ophthalmology · doi:10.1159/isbn.978-3-318-01439-6 · Cited by 5,583
Global prevalence and major risk factors of diabetic retinopathy
Yau, J. W. Y., Rogers, S. L., Kawasaki, R., Lamoureux, E. L., Kowalski, J. W., Bek, T., Chen, S.-J., Dekker, J. M., Fletcher, A., Grauslund, J., et al. (2012) · Diabetes Care · doi:10.2337/dc11-1909 · Cited by 4,985
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Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss
Lee, R., Wong, T. Y., & Sabanayagam, C. (2015) · Eye and Vision · doi:10.1186/s40662-015-0026-2 · Cited by 1,569
FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE
Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., Gain, P., Ordonez, R., Massin, P., Erginay, A., et al. (2014) · Image Analysis & Stereology · doi:10.5566/ias.1155 · Cited by 1,424
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DUNet: A deformable network for retinal vessel segmentation
Jin, Q., Meng, Z., Pham, T. D., Chen, Q., Wei, L., & Su, R. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.04.025 · Cited by 867
Automated EEG-based screening of depression using deep convolutional neural network
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adeli, H., & Subha, D. P. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.012 · Cited by 650
Self-citationApplications of deep learning in fundus images: A review
Li, T., Bo, W., Hu, C., Kang, H., Liu, H., Wang, K., & Fu, H. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101971 · Cited by 396
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Metadata
- Authors
- Tan, J. H., Fujita, H., Sivaprasad, S., Bhandary, S. V., Rao, A. K., Chua, K. C., & Acharya, U. R.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 420
- Pages
- 66-76
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2749049648
- Cited by
- 266
- References
- 32
- FWCI
- 19.12
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Tan, J. H., Fujita, H., Sivaprasad, S., Bhandary, S. V., Rao, A. K., Chua, K. C., & Acharya, U. R. (2017). Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Information Sciences. https://doi.org/10.1016/j.ins.2017.08.050
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
