The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image
Open accessAbstract
Glaucoma is a chronic eye disease that leads to irreversible vision loss. Most of the existing automatic screening methods first segment the main structure and subsequently calculate the clinical measurement for the detection and screening of glaucoma. However, these measurement-based methods rely heavily on the segmentation accuracy and ignore various visual features. In this paper, we introduce a deep learning technique to gain additional image-relevant information and screen glaucoma from the fundus image directly. Specifically, a novel disc-aware ensemble network for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region. Four deep streams on different levels and modules are, respectively, considered as global image stream, segmentation-guided network, local disc region stream, and disc polar transformation stream. Finally, the output probabilities of different streams are fused as the final screening result. The experiments on two glaucoma data sets (SCES and new SINDI data sets) show that our method outperforms other state-of-the-art algorithms.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
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
Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040
Tham, Y.-C., Li, X., Wong, T. Y., Quigley, H. A., Aung, T., & Cheng, C.-Y. (2014) · Ophthalmology · doi:10.1016/j.ophtha.2014.05.013 · Cited by 7,159
Ting, D. S. W., Cheung, C. Y.-L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I. Y., Lee, S. Y., et al. (2017) · JAMA · doi:10.1001/jama.2017.18152 · Cited by 2,402
Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation
Fu, H., Cheng, J., Xu, Y., Wong, D. W. K., Liu, J., & Cao, X. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2791488 · Cited by 834
Self-citationGeneralized Overlap Measures for Evaluation and Validation in Medical Image Analysis
Crum, W. R., Camara, O., & Hill, D. L. G. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.880587 · Cited by 776
Risk Factors for Incident Open-angle Glaucoma
Leske, M. C., Wu, S.-Y., Hennis, A., Honkanen, R., & Nemesure, B. (2008) · Ophthalmology · doi:10.1016/j.ophtha.2007.03.017 · Cited by 774
Ophthalmoscopic Evaluation of the Optic Nerve Head
Jonas, J. B., Budde, W. M., & Panda-Jonas, S. (1999) · Survey of Ophthalmology · doi:10.1016/s0039-6257(98)00049-6 · Cited by 607
Cited by
A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection
Li, L., Xu, M., Liu, H., Li, Y., Wang, X., Jiang, L., Wang, Z., Fan, X., & Wang, N. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2927226 · Cited by 303
Liu, H., Wang, L., Nan, Y., Jin, F., Wang, Q., & Pu, J. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.05.005 · Cited by 135
Rotation-Oriented Collaborative Self-Supervised Learning for Retinal Disease Diagnosis
Li, X., Hu, X., Qi, X., Yu, L., Zhao, W., Heng, P.-A., & Xing, L. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3075244 · Cited by 105
Rogers, T. W., Jaccard, N., Carbonaro, F., Lemij, H. G., Vermeer, K. A., Reus, N. J., & Trikha, S. (2019) · Eye · doi:10.1038/s41433-019-0510-3 · Cited by 47
Metadata
- Authors
- Fu, H., Cheng, J., Xu, Y., Zhang, C., Wong, D. W. K., Liu, J., & Cao, X.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2018-11
- Volume
- 37
- Issue
- 11
- Pages
- 2493-2501
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2804179870
- Cited by
- 394
- References
- 23
- FWCI
- 24.73
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image. https://app.cochat.ai/papers/10.1109/tmi.2018.2837012
- The paper
- Fu, H., Cheng, J., Xu, Y., Zhang, C., Wong, D. W. K., Liu, J., & Cao, X. (2018). Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2018.2837012
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.
