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Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images capture high-resolution views of the retina with typically 200 spanning degrees.
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Y. Kato, M. Inoue, and A. Hirakata, “Quantitative comparisons of ultra-widefield images of model eye obtained with optos® 200tx and optos® california,” BMC ophthalmology , vol. 19, pp. 1–6, 2019
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L. Ding, A. E. Kuriyan et al. , “Weakly-supervised vessel detection in ultra-widefield fundus photography via iterative multi-modal registration and learning,” IEEE Transactions on Medical Imaging , vol. 40, no. 10, pp. 2748–2758, 2020
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L. Ju, X. Wang, X. Zhao et al. , “Leveraging regular fundus images for training uwf fundus diagnosis models via adversarial learning and pseudo-labeling,” IEEE Transactions on Medical Imaging , vol. 40, no. 10, pp. 2911–2925, 2021
2021
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H. Wu, W. Wang, J. Zhong, B. Lei, Z. Wen, and J. Qin, “Scs-net: A scale and context sensitive network for retinal vessel segmentation,” Medical Image Analysis , vol. 70, p. 102025, 2021
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C. Guo et al. , “Sa-unet: Spatial attention u-net for retinal vessel segmentation,” in 2020 25th international conference on pattern recognition (ICPR) . IEEE, 2021, pp. 1236–1242
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Y. Sang, J. Sun, S. Wang, H. Qi, and K. Li, “Super-resolution and infection edge detection co-guided learning for covid-19 ct segmentation,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 1665–1669
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L. Ding, T. D. Kang et al. , “Combining feature correspondence with parametric chamfer alignment: hybrid two-stage registration for ultra-widefield retinal images,” IEEE Transactions on Biomedical Engineering , vol. 70, no. 2, pp. 523–532, 2022
2022
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H. Hanssen, L. Streese, and W. Vilser, “Retinal vessel diameters and function in cardiovascular risk and disease,” Progress in retinal and eye research , vol. 91, p. 101095, 2022
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Y. Li, Y. Zhang et al. , “Dual encoder-based dynamic-channel graph convolutional network with edge enhancement for retinal vessel segmentation,” IEEE Transactions on Medical Imaging , vol. 41, no. 8, pp. 1975–1989, 2022
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Y. Hu et al. , “Supervessel: Segmenting high-resolution vessel from low-resolution retinal image,” in Chinese Conference on Pattern Recognition and Computer Vision . Springer, 2022, pp. 178–190
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J. Zhang et al. , “Hard exudate segmentation supplemented by super-resolution with multi-scale attention fusion module,” in 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) . IEEE, 2022, pp. 1375–1380
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Z. Qiu, Y. Hu et al. , “Rethinking dual-stream super-resolution semantic learning in medical image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
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2023
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J. Ryu et al. , “Segr-net: A deep learning framework with multi-scale feature fusion for robust retinal vessel segmentation,” Computers in Biology and Medicine , vol. 163, p. 107132, 2023
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W. Zhou, W. Bai, J. Ji, Y. Yi, N. Zhang, and W. Cui, “Dual-path multi-scale context dense aggregation network for retinal vessel segmentation,” Computers in Biology and Medicine , vol. 164, p. 107269, 2023
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J. Lin, X. Huang et al. , “Stimulus-guided adaptive transformer network for retinal blood vessel segmentation in fundus images,” Medical Image Analysis , vol. 89, p. 102929, 2023
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K. Chen et al. , “Rsmamba: Remote sensing image classification with state space model,” IEEE Geoscience and Remote Sensing Letters , 2024
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M. Liu et al. , “Aa-wgan: Attention augmented wasserstein generative adversarial network with application to fundus retinal vessel segmentation,” Computers in Biology and Medicine , vol. 158, p. 106874, 2023
2023
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X. Li, J. Song, W. Jiao, and Y. Zheng, “Minet: Multi-scale input network for fundus microvascular segmentation,” Computers in Biology and Medicine , vol. 154, p. 106608, 2023
2023
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M. Tian, H. Wang, X. Liu, Y. Ye, G. Ouyang, Y. Shen, Z. Li, X. Wang, and S. Wu, “Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy by deep learning networks,” Medical Physics , vol. 50, no. 10, pp. 6354–6365, 2023
2023
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Q.-Q. Tang et al. , “Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images,” International Journal of Ophthalmology , vol. 17, no. 1, p. 188, 2024
2024
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2024
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H. Zhang, Y. Zhu et al. , “A survey on visual mamba,” Applied Sciences , vol. 14, no. 13, p. 5683, 2024
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Y. Liu, Y. Tian et al. , “Vmamba: Visual state space model,” arXiv preprint arXiv:2401.10166 , 2024
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B. Kim, Y. Oh et al. , “C-darl: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation,” Medical Image Analysis , vol. 91, p. 103022, 2024
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H. Wang, J. Chen, S. Zhang, Y. He, J. Xu, M. Wu, J. He, W. Liao, and X. Luo, “Dual-reference source-free active domain adaptation for nasopharyngeal carcinoma tumor segmentation across multiple hospitals,” IEEE Transactions on Medical Imaging , 2024
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H. Wang et al. , “Video-instrument synergistic network for referring video instrument segmentation in robotic surgery,” IEEE Transactions on Medical Imaging , 2024
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