The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
Abstract
The precise segmentation of retinal blood vessels is of great significance for early diagnosis of eye-related diseases such as diabetes and hypertension. In this work, we propose a lightweight network named Spatial Attention U-Net (SA-UNet) that does not require thousands of annotated training samples and can be utilized in a data augmentation manner to use the available annotated samples more efficiently. SA-UNet introduces a spatial attention module which infers the attention map along the spatial dimension, and multiplies the attention map by the input feature map for adaptive feature refinement. In addition, the proposed network employs structured dropout convolutional blocks instead of the original convolutional blocks of U-Net to prevent the network from overfitting. We evaluate SA-UNet based on two benchmark retinal datasets: the Vascular Extraction (DRIVE) dataset and the Child Heart and Health Study (CHASE_DB1) dataset. The results show that the proposed SA-UNet achieves state-of-the-art performance on both datasets. The implementation and the trained networks are available on Github1.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
CBAM: Convolutional Block Attention Module
Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
Ghiasi, G., Lin, T.-Y., & Le, Q. V. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00720 · Cited by 2,099
Segmenting Retinal Blood Vessels WithDeep Neural Networks
Liskowski, P., & Krawiec, K. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2546227 · Cited by 890
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
Orlando, J. I., Prokofyeva, E., & Blaschko, M. B. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2535311 · Cited by 503
DropBlock: A regularization method for convolutional networks
Ghiasi, G., Lin, T.-Y., & Le, Q. V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.12890 · Cited by 459
Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation
Yan, Z., Yang, X., & Cheng, K.-T. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2828137 · Cited by 428
Attention Guided Network for Retinal Image Segmentation
Zhang, S., Fu, H., Yan, Y., Zhang, Y., Wu, Q., Yang, M., Tan, M., & Xu, Y. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32239-7_88 · Cited by 201
Cited by
Sun, G., Pan, Y., Kong, W., Xu, Z., Ma, J., Racharak, T., Nguyen, L.-M., & Xin, J. (2024) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2024.1398237 · Cited by 339
Metadata
- Authors
- Guo, C., Szemenyei, M., Yi, Y., Wang, W., Chen, B., & Fan, C.
- Venue
- 2020 25th International Conference on Pattern Recognition (ICPR)
- Publisher
- IEEE
- Published
- 2021-01-10
- Pages
- 1236-1242
- ISSN
- 1041-3278 (ISSN-L)
- OpenAlex
- W3161081823
- Cited by
- 431
- References
- 18
- FWCI
- 94.71
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Guo, C., Szemenyei, M., Yi, Y., Wang, W., Chen, B., & Fan, C. (2021). SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation. 2020 25th International Conference on Pattern Recognition (ICPR). https://doi.org/10.1109/icpr48806.2021.9413346
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