The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Lightweight Attention Convolutional Neural Network for Retinal Vessel Image Segmentation
Abstract
Retinal vessel image is an important biological information that can be used for personal identification in the social security domain, and for disease diagnosis in the medical domain. While automatic vessel image segmentation is essential, it is also a challenging task because the retinal vessels have complex topological structures, and the retinal vessels vary in size and shape. In recent years, image segmentation based on the deep learning technique has become a mainstream method. Unfortunately, the existing methods cannot make the best use of the global information, and the model complexity is high. In this article, a convolutional neural network integrated with the attention mechanism is proposed. The overall network structure consists of a basic U-Net and an attention module, and the latter is used to capture global information and to enhance features by placing it in the process of feature fusion. Experiment results on five public datasets show that the proposed scheme outperforms other existing mainstream approaches, and most of the performance indicators are in the leading positions. More importantly, the proposed method has a significant reduction in the number of parameters.
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Cites
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
Dual Attention Network for Scene Segmentation
Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., & Lu, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00326 · Cited by 7,351
Ridge-based vessel segmentation in color images of the retina
Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & van Ginneken, B. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.825627 · Cited by 4,202
Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response
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An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation
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Quo vadis artificial intelligence?
Jiang, Y., Li, X., Luo, H., Yin, S., & Kaynak, O. (2022) · Discover Artificial Intelligence · doi:10.1007/s44163-022-00022-8 · Cited by 544
Self-citationDeep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds
Li, X., Li, M., Yan, P., Li, G., Jiang, Y., Luo, H., & Yin, S. (2023) · International Journal of Network Dynamics and Intelligence · doi:10.53941/ijndi0201006 · Cited by 269
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Metadata
- Authors
- Li, X., Jiang, Y., Li, M., & Yin, S.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2021-03
- Volume
- 17
- Issue
- 3
- Pages
- 1958-1967
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W3025177399
- Cited by
- 265
- References
- 39
- FWCI
- 17.48
- Type
- journal article
- Language
- en
- Open access
- closed
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