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Segmentation is vital for ophthalmology image analysis.
doi:10.1109/TBME.2012.2205687
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A. Budai, R. Bock, A. Maier, J. Hornegger, G. Michelson, Robust vessel segmentation in fundus images, International journal of biomedical imaging 2013
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O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: N. Navab, J. Hornegger, W. M. Wells, A. F. Frangi (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Springer International Publishing, Cham, 2015, pp. 234–241
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Y. Li, X. Xia, Y. M. Paulus, Advances in retinal optical imaging, in: Photonics, Vol. 5, MDPI, 2018, p. 9
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A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, S. Chintala, Pytorch: An imperative style, high-performance deep learning library, in: H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems 32, Curran Associates, Inc., 2019, pp. 8024–8035
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Y. Ma, H. Hao, J. Xie, H. Fu, J. Zhang, J. Yang, Z. Wang, J. Liu, Y. Zheng, Y. Zhao, Rose: A retinal oct-angiography vessel segmentation dataset and new model, IEEE Transactions on Medical Imaging 40 (3) (2021) 928–939 · 2020
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M. Li, Y. Chen, Z. Ji, K. Xie, S. Yuan, Q. Chen, S. Li, Image projection network: 3d to 2d image segmentation in octa images, IEEE Transactions on Medical Imaging 39 (11) (2020) 3343–3354
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M. Melinščak, M. Radmilović, Z. Vatavuk, S. Lončarić, Annotated retinal optical coherence tomography images (aroi) database for joint retinal layer and fluid segmentation, Automatika 62 (3-4) (2021) 375–385
2021
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M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, S.-N. Lim, Visual prompt tuning, in: S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, T. Hassner (Eds.), Computer Vision – ECCV 2022, Springer Nature Switzerland, Cham, 2022, pp. 709–727
2022
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doi:10.1109/BIBM55620.2022.9995545
J. Zhang, X. Chen, Z. Qiu, M. Yang, Y. Hu, J. Liu, Hard exudate segmentation supplemented by super-resolution with multi-scale attention fusion module, in: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022, pp. 1375–1380 · 2022
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M. A. Mazurowski, H. Dong, H. Gu, J. Yang, N. Konz, Y. Zhang, Segment anything model for medical image analysis: an experimental study (2023) · 2023
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S. He, R. Bao, J. Li, P. E. Grant, Y. Ou, Accuracy of segment-anything model (sam) in medical image segmentation tasks (2023) · 2023
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T. Yu, R. Feng, R. Feng, J. Liu, X. Jin, W. Zeng, Z. Chen, Inpaint anything: Segment anything meets image inpainting (2023) · 2023
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S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu, L. Zhang, Grounding dino: Marrying dino with grounded pre-training for open-set object detection, 2023
2023
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, R. Girshick, Segment anything (2023) · 2023
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M. Oquab, T. Darcet, T. Moutakanni, H. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby, M. Assran, N. Ballas, W. Galuba, R. Howes, P.-Y. Huang, S.-W. Li, I. Misra, M. Rabbat, V. Sharma, G. Synnaeve, H. Xu, H. Jegou, J. Mairal, P. Labatut, A. Joulin, P. Bojanowski, Dinov2: Learning robust visual features without supervision (2023) · 2023
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K. Jin, X. Huang, J. Zhou, Y. Li, Y. Yan, Y. Sun, Q. Zhang, Y. Wang, J. Ye, Fives: A fundus image dataset for artificial intelligence based vessel segmentation, Scientific Data
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P. Porwal, S. Pachade, R. Kamble, M. Kokare, G. Deshmukh, V. Sahasrabuddhe, F. Meriaudeau, Indian diabetic retinopathy image dataset (idrid): A database for diabetic retinopathy screening research , Data 3 (3)
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P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, G. Neubig, Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing, ACM Computing Surveys 55 (9) (2023) 1–35
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