Research on attention-enhanced networks for subtype classification of age-related macular degeneration in optical coherence tomography_Journal of Biomedical Engineering

Authors：

 CHEN Minghui ¹ , YANG Wenyi ¹ , XU Shiyi ¹ , LU Yanqi ¹ , YANG Zhengqi ¹ , LI Fugang ² , GU Zhensheng ³

1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
2. Shanghai Upper Bio-Tech Pharma Co., LTD, Shanghai 200003, P. R. China;
3. Department of Ophthalmology, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, P. R. China;

Corresponding?author：

CHEN Minghui, Email: cmhui.43@163.com

Keywords：

Optical coherence tomography; Age-related macular degeneration; Image classification; Key point location; Two-dimensional relative self-attention mechanism

DOI：

10.7507/1001-5515.202408029

Video：

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Abstract Full text Figures/Tables Video References Cited by

Subtype classification of age-related macular degeneration (AMD) based on optical coherence tomography (OCT) images serves as an effective auxiliary tool for clinicians in diagnosing disease progression and formulating treatment plans. To improve the classification accuracy of AMD subtypes, this study proposes a keypoint-based, attention-enhanced residual network (KPA-ResNet). The proposed architecture adopts a 50-layer residual network (ResNet-50) as the backbone, preceded by a keypoint localization module based on heatmap regression to outline critical lesion regions. A two-dimensional relative self-attention mechanism is incorporated into convolutional layers to enhance the representation of key lesion areas. Furthermore, the network depth is appropriately increased and an improved residual module, ConvNeXt, is introduced to enable comprehensive extraction of high-dimensional features and enrich the detail of lesion boundary contours, ultimately achieving higher classification accuracy of AMD subtypes. Experimental results demonstrate that KPA-ResNet achieves significant improvements in overall classification accuracy compared with conventional convolutional neural networks. Specifically, for the wet AMD subtypes, the classification accuracies for inactive choroidal neovascularization (CNV) and active CNV reach 92.8% and 95.2%, respectively, representing substantial improvement over ResNet-50. These findings validate the superior performance of KPA-ResNet in AMD subtype classification tasks. This work provides a high-accuracy, generalizable network architecture for OCT-based AMD subtype classification and offers new insights into integrating attention mechanisms with convolutional neural networks in ophthalmic image analysis.

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2.	Miotto S, Zemella N, Gusson E, et al. Morphologic criteria of lesion activity in neovascular age-related macular degeneration: a consensus article. Journal of Ocular Pharmacology and Therapeutics, 2018, 34(3): 298-308.
3.	邵毅, 溫佳怡, 令倩. 年齡相關性黃斑變性診斷與治療規范: 2022年英國皇家眼科醫學會指南解讀. 眼科新進展, 2023, 43(2): 85-88.
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5.	Deng Y, Qiao L, Du M, et al. Age-related macular degeneration: epidemiology, genetics, pathophysiology, diagnosis, and targeted therapy. Genes Dis, 2021, 9(1): 62-79.
6.	Wang J, He Y, Fang W, et al. Unsupervised domain adaptation model for lesion detection in retinal OCT images. Phys Med Biol, 2021, 66: 215006.
7.	Lupidi M, Cerquaglia A, Chhablani J, et al. Optical coherence tomography angiography in age-related macular degeneration: the game changer. Eur J Ophthalmol, 2018, 28(4): 349-357.
8.	Wang J, Li W, Chen Y, et al. Weakly supervised anomaly segmentation in retinal OCT images using an adversarial learning approach. Biomedical Optics Express, 2021, 12(8): 4713-4729.
9.	汪榮貴, 姚旭晨, 楊娟, 等. 基于深度遷移學習的微型細粒度圖像分類. 光電工程, 2019, 46(6): 26-35.
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13.	Wang W, Xu Z, Yu W, et al. Two-stream CNN with loose pair training for multi-modal AMD categorization//Medical Image Computing and Computer Assisted Intervention–MICCAI 2019, Shenzhen: Springer International Publishing, 2019: 156-164.
14.	Vaghefi E, Hill S, Kersten H M, et al. Multi-modal retinal image analysis via deep learning for the diagnosis of intermediate dry age-related macular degeneration: a feasibility study. Journal of Ophthalmology, 2020, 2020: 7493419.
15.	Xu Z, Wang W, Yang J, et al. Automated diagnoses of age-related macular degeneration and polypoidal choroidal vasculopathy using bi-modal deep convolutional neural networks. British Journal of Ophthalmology, 2021, 105(4): 561-566.
16.	Hwang D K, Hsu C C, Chang K J, et al. Artificial intelligence-based decision-making for age-related macular degeneration. Theranostics, 2019, 9(1): 232-245.
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18.	Ma Z, Xie Q, Xie P, et al. HCTNet: a hybrid ConvNet-Transformer network for retinal optical coherence tomography image classification. Biosensors, 2022, 12(7): 542.
19.	楊文逸, 陳明惠, 吳玉全, 等. 采用自注意力機制的OCT圖像AMD亞型分類研究. 光學技術, 2024, 50(1): 112-119.
20.	許偉濠, 張伯泉, 劉銀萍. 基于熱力圖和注意力機制的單目6D姿態估計算法. 微電子學與計算機, 2023, 40(7): 45-54.
21.	Shaw P, Uszkoreit J, Vaswani A. Self-attention with relative position representations. arXiv preprint, 2018, arXiv: 1803.02155.
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23.	Hu J, Shen L, Sun G. Squeeze-and-excitation networks//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City: IEEE, 2018: 7132-7141.
24.	Hu J, Shen L, Albanie S, et al. Gather-excite: exploiting feature context in convolutional neural networks//32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal: NIPS, 2018, 31: 1-11.
25.	Park J, Woo S, Lee J Y, et al. BAM: bottleneck attention module. arXiv preprint, 2018, arXiv: 1807.06514.
26.	Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module//Proceedings of the European Conference on Computer Vision (ECCV), Munich: IEEE, 2018: 3-19.
27.	Zhang H, Yang S, Zhang X. Automatic recognition and classification of microalgae using an inception-v3 convolution neural network model. International Journal of Environmental Science and Technology, 2023, 21(4): 4625-4634.
28.	Anand R, Lakshmi V S, Pandey B K. An enhanced ResNet-50 deep learning model for arrhythmia detection using electrocardiogram biomedical indicators. Evolving Systems, 2023, 15(1): 83-97.
29.	Saluja S ,Trivedi M C ,Sarangdevot S S. Advancing glioma diagnosis: Integrating custom U-Net and VGG-16 for improved grading in MR imaging. Math Biosci Eng, 2024, 21(3): 4328-4350.

1. Tsang J Y, Wright A, Carr M J, et al. Risk of falls and fractures in individuals with cataract, age-related macular degeneration, or glaucoma. JAMA Ophthalmol, 2024, 142(2): 96-106.
2. Miotto S, Zemella N, Gusson E, et al. Morphologic criteria of lesion activity in neovascular age-related macular degeneration: a consensus article. Journal of Ocular Pharmacology and Therapeutics, 2018, 34(3): 298-308.
3. 邵毅, 溫佳怡, 令倩. 年齡相關性黃斑變性診斷與治療規范: 2022年英國皇家眼科醫學會指南解讀. 眼科新進展, 2023, 43(2): 85-88.
4. Bakri S J, Thorne J E, Ho A C, et al. Safety and efficacy of anti-vascular endothelial growth factor therapies for neovascular age-related macular degeneration: a report by the American Academy of Ophthalmology. Ophthalmology, 2019, 126(1): 55-63.
5. Deng Y, Qiao L, Du M, et al. Age-related macular degeneration: epidemiology, genetics, pathophysiology, diagnosis, and targeted therapy. Genes Dis, 2021, 9(1): 62-79.
6. Wang J, He Y, Fang W, et al. Unsupervised domain adaptation model for lesion detection in retinal OCT images. Phys Med Biol, 2021, 66: 215006.
7. Lupidi M, Cerquaglia A, Chhablani J, et al. Optical coherence tomography angiography in age-related macular degeneration: the game changer. Eur J Ophthalmol, 2018, 28(4): 349-357.
8. Wang J, Li W, Chen Y, et al. Weakly supervised anomaly segmentation in retinal OCT images using an adversarial learning approach. Biomedical Optics Express, 2021, 12(8): 4713-4729.
9. 汪榮貴, 姚旭晨, 楊娟, 等. 基于深度遷移學習的微型細粒度圖像分類. 光電工程, 2019, 46(6): 26-35.
10. 齊永鋒, 呂雪超, 裴曉旭, 等. 基于生成對抗網絡的高光譜圖像分類. 光電子·激光, 2021, 32(12): 1285-1292.
11. Lee C S, Baughman D M, Lee A Y. Deep learning is effective for the classification of OCT images of normal versus age-related macular degeneration. Ophthalmol Retina, 2017, 1(4): 322-327.
12. Serener A, Serte S. Dry and wet age-related macular degeneration classification using oct images and deep learning//2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), Istanbul: IEEE, 2019: 1-4.
13. Wang W, Xu Z, Yu W, et al. Two-stream CNN with loose pair training for multi-modal AMD categorization//Medical Image Computing and Computer Assisted Intervention–MICCAI 2019, Shenzhen: Springer International Publishing, 2019: 156-164.
14. Vaghefi E, Hill S, Kersten H M, et al. Multi-modal retinal image analysis via deep learning for the diagnosis of intermediate dry age-related macular degeneration: a feasibility study. Journal of Ophthalmology, 2020, 2020: 7493419.
15. Xu Z, Wang W, Yang J, et al. Automated diagnoses of age-related macular degeneration and polypoidal choroidal vasculopathy using bi-modal deep convolutional neural networks. British Journal of Ophthalmology, 2021, 105(4): 561-566.
16. Hwang D K, Hsu C C, Chang K J, et al. Artificial intelligence-based decision-making for age-related macular degeneration. Theranostics, 2019, 9(1): 232-245.
17. E Haihong, Ding J, Yuan L. SAE-wAMD: a self-attention enhanced convolution neural network for fine-grained classification of wet age-related macular degeneration using OCT image//2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), Xi’an: IEEE, 2023: 619-627.
18. Ma Z, Xie Q, Xie P, et al. HCTNet: a hybrid ConvNet-Transformer network for retinal optical coherence tomography image classification. Biosensors, 2022, 12(7): 542.
19. 楊文逸, 陳明惠, 吳玉全, 等. 采用自注意力機制的OCT圖像AMD亞型分類研究. 光學技術, 2024, 50(1): 112-119.
20. 許偉濠, 張伯泉, 劉銀萍. 基于熱力圖和注意力機制的單目6D姿態估計算法. 微電子學與計算機, 2023, 40(7): 45-54.
21. Shaw P, Uszkoreit J, Vaswani A. Self-attention with relative position representations. arXiv preprint, 2018, arXiv: 1803.02155.
22. Liu Z, Lin Y, Cao Y, et al. Swin transformer: hierarchical vision transformer using shifted windows//Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Montreal: IEEE, 2021: 10012-10022.
23. Hu J, Shen L, Sun G. Squeeze-and-excitation networks//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City: IEEE, 2018: 7132-7141.
24. Hu J, Shen L, Albanie S, et al. Gather-excite: exploiting feature context in convolutional neural networks//32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal: NIPS, 2018, 31: 1-11.
25. Park J, Woo S, Lee J Y, et al. BAM: bottleneck attention module. arXiv preprint, 2018, arXiv: 1807.06514.
26. Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module//Proceedings of the European Conference on Computer Vision (ECCV), Munich: IEEE, 2018: 3-19.
27. Zhang H, Yang S, Zhang X. Automatic recognition and classification of microalgae using an inception-v3 convolution neural network model. International Journal of Environmental Science and Technology, 2023, 21(4): 4625-4634.
28. Anand R, Lakshmi V S, Pandey B K. An enhanced ResNet-50 deep learning model for arrhythmia detection using electrocardiogram biomedical indicators. Evolving Systems, 2023, 15(1): 83-97.
29. Saluja S ,Trivedi M C ,Sarangdevot S S. Advancing glioma diagnosis: Integrating custom U-Net and VGG-16 for improved grading in MR imaging. Math Biosci Eng, 2024, 21(3): 4328-4350.

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