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Early-stage diabetic retinopathy (DR) presents challenges in clinical diagnosis due to inconspicuous and minute microangioma lesions, resulting in limited research in this area.
D. Man and A. Vision, “A computational investigation into the human representation and processing of visual information,” WH San Francisco: Freeman and Company, San Francisco , vol. 1, 1982
1982
Earlier work this paper cites.
D. L. Sackett, W. M. Rosenberg, J. M. Gray, R. B. Haynes, and W. S. Richardson, “Evidence based medicine,” BMJ: British Medical Journal , vol. 313, no. 7050, p. 170, 1996
1996
Earlier work this paper cites.
I. C. Consortium et al. , “Image technology colour management-architecture, profile format, and data structure,” Specification ICC. 1: 2004-10 (Profile version 4.2. 0.0) , 2004
2004
Earlier work this paper cites.
R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, “Frequency-tuned salient region detection,” in 2009 IEEE conference on computer vision and pattern recognition . IEEE, 2009, pp. 1597–1604
2009
Earlier work this paper cites.
M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE reviews in biomedical engineering , vol. 3, pp. 169–208, 2010
2010
Earlier work this paper cites.
R. Andersson, M. Nyström, and K. Holmqvist, “Sampling frequency and eye-tracking measures: how speed affects durations, latencies, and more,” Journal of Eye Movement Research , vol. 3, no. 3, 2010
2010
Earlier work this paper cites.
W. Sui and D. Zhang, “Four methods for roundness evaluation,” Physics Procedia , vol. 24, pp. 2159–2164, 2012
2012
Earlier work this paper cites.
J. Zhang, S. Sclaroff, Z. Lin, X. Shen, B. Price, and R. Mech, “Minimum barrier salient object detection at 80 fps,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1404–1412
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros et al. , “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” jama , vol. 316, no. 22, pp. 2402–2410, 2016
2016
Earlier work this paper cites.
G. Quellec, K. Charriere, Y. Boudi, B. Cochener, and M. Lamard, “Deep image mining for diabetic retinopathy screening,” Medical image analysis , vol. 39, pp. 178–193, 2017
2017
Earlier work this paper cites.
P. Porwal, S. Pachade, R. Kamble, M. Kokare, G. Deshmukh, V. Sahasrabuddhe, and F. Meriaudeau, “Indian diabetic retinopathy image dataset (idrid): a database for diabetic retinopathy screening research,” Data , vol. 3, no. 3, p. 25, 2018
2018
Earlier work this paper cites.
J. N. Stember, H. Celik, E. Krupinski, P. D. Chang, S. Mutasa, B. J. Wood, A. Lignelli, G. Moonis, L. Schwartz, S. Jambawalikar et al. , “Eye tracking for deep learning segmentation using convolutional neural networks,” Journal of digital imaging , vol. 32, pp. 597–604, 2019
2019
Earlier work this paper cites.
J. N. Stember, H. Celik, D. Gutman, N. Swinburne, R. Young, S. Eskreis-Winkler, A. Holodny, S. Jambawalikar, B. J. Wood, P. D. Chang et al. , “Integrating eye tracking and speech recognition accurately annotates mr brain images for deep learning: proof of principle,” Radiology: Artificial Intelligence , vol. 3, no. 1, p. e200047, 2020
2020
Cited alongside, same era.
P. Porwal, S. Pachade, M. Kokare, G. Deshmukh, J. Son, W. Bae, L. Liu, J. Wang, X. Liu, L. Gao et al. , “Idrid: Diabetic retinopathy–segmentation and grading challenge,” Medical image analysis , vol. 59, p. 101561, 2020
2020
Cited alongside, same era.
A. Karargyris, S. Kashyap, I. Lourentzou, J. T. Wu, A. Sharma, M. Tong, S. Abedin, D. Beymer, V. Mukherjee, E. A. Krupinski et al. , “Creation and validation of a chest x-ray dataset with eye-tracking and report dictation for ai development,” Scientific data , vol. 8, no. 1, p. 92, 2021
2021
Cited alongside, same era.
2023
Closest in time.
M. A. Mazurowski, H. Dong, H. Gu, J. Yang, N. Konz, and Y. Zhang, “Segment anything model for medical image analysis: an experimental study,” Medical Image Analysis , vol. 89, p. 102918, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
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Q. Wei, X. Li, W. Yu, X. Zhang, Y. Zhang, B. Hu, B. Mo, D. Gong, N. Chen, D. Ding et al. , “Learn to segment retinal lesions and beyond,” in 2020 25th International Conference on Pattern Recognition (ICPR) . IEEE, 2021, pp. 7403–7410
2021
Cited alongside, same era.
S. Wang, X. Ouyang, T. Liu, Q. Wang, and D. Shen, “Follow my eye: using gaze to supervise computer-aided diagnosis,” IEEE Transactions on Medical Imaging , vol. 41, no. 7, pp. 1688–1698, 2022
2022
Cited alongside, same era.
S. Huang, J. Li, Y. Xiao, N. Shen, and T. Xu, “Rtnet: relation transformer network for diabetic retinopathy multi-lesion segmentation,” IEEE Transactions on Medical Imaging , vol. 41, no. 6, pp. 1596–1607, 2022
2022
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
J. Qiu, L. Li, J. Sun, J. Peng, P. Shi, R. Zhang, Y. Dong, K. Lam, F. P.-W. Lo, B. Xiao et al. , “Large ai models in health informatics: Applications, challenges, and the future,” IEEE Journal of Biomedical and Health Informatics , 2023
2023
Cited alongside, same era.
P. Shi, J. Qiu, S. M. D. Abaxi, H. Wei, F. P.-W. Lo, and W. Yuan, “Generalist vision foundation models for medical imaging: A case study of segment anything model on zero-shot medical segmentation,” Diagnostics , vol. 13, no. 11, p. 1947, 2023
2023
Cited alongside, same era.
2023
Closest in time.
J. Ma and B. Wang, “Segment anything in medical images,” arXiv preprint arXiv:2304.12306 , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
H. Wang, P. Cao, J. Yang, and O. Zaiane, “Mca-unet: multi-scale cross co-attentional u-net for automatic medical image segmentation,” Health Information Science and Systems , vol. 11, no. 1, p. 10, 2023
2023
Closest in time.
X. Wang, Y. Fang, S. Yang, D. Zhu, M. Wang, J. Zhang, J. Zhang, J. Cheng, K.-y. Tong, and X. Han, “Clc-net: Contextual and local collaborative network for lesion segmentation in diabetic retinopathy images,” Neurocomputing , vol. 527, pp. 100–109, 2023
2023
Closest in time.
H. Jiang, K. Yang, M. Gao, D. Zhang, H. Ma, and W. Qian, “An interpretable ensemble deep learning model for diabetic retinopathy disease classification,” in 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC) . IEEE, 2019, pp. 2045–2048
2048
Closest in time.