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Medical image segmentation aims to delineate the anatomical or pathological structures of interest, playing a crucial role in clinical diagnosis.
“Learning for structured prediction using approximate subgradient descent with working sets,”
A. Lucchi, Y. Li, and P. Fua, · 1994
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“Learning to detect natural image boundaries using local brightness, color, and texture cues,”
D.R. Martin, C.C. Fowlkes, and J. Malik, · 2004
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“A novel spatio-temporal video object segmentation algorithm,”
S. Zhu, X. Xia, Q. Zhang, and K. Belloulata, · 2008
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“The pascal visual object classes (voc) challenge,”
M. Everingham, L. Van Gool, C.K. Williams, J. Winn, and A. Zisserman, · 2010
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“Segmentation of moving objects by long term video analysis,”
P. Ochs, J. Malik, and T. Brox, · 2013
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“Video segmentation by non-local consensus voting.,”
A. Faktor and M. Irani, · 2014
Earlier work this paper cites.
“U-net: Convolutional networks for biomedical image segmentation,”
O. Ronneberger, P. Fischer, and T. Brox, · 2015
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“A benchmark dataset and evaluation methodology for video object segmentation,”
F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung, · 2016
Earlier work this paper cites.
“One-shot video object segmentation,”
S. Caelles, K.K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, and L. Van Gool, · 2017
Cited alongside, same era.
“Monet: Deep motion exploitation for video object segmentation,”
H. Xiao, J. Feng, G. Lin, Y. Liu, and M. Zhang, · 2018
Cited alongside, same era.
“Video object segmentation using space-time memory networks,”
S. Oh, J. Lee, N. Xu, and S. Kim, · 2019
Cited alongside, same era.
“Anchor diffusion for unsupervised video object segmentation,”
Z. Yang, Q. Wang, L. Bertinetto, W. Hu, S. Bai, and P.H. Torr, · 2019
Cited alongside, same era.
“Deep learning for segmentation using an open large-scale dataset in 2d echocardiography,”
S. Leclerc, E. Smistad, J. Pedrosa, A. Østvik, F. Cervenansky, F. Espinosa, T. Espeland, E.A.R. Berg, P.M. Jodoin, T. Grenier, et al., · 2019
Cited alongside, same era.
“Motion-attentive transition for zero-shot video object segmentation,”
“XMem: Long-term video object segmentation with an atkinson-shiffrin memory model,”
H.K. Cheng and A.G. Schwing, · 2022
Later among the works it cites.
“Conv-adapter: Exploring parameter efficient transfer learning for convnets,”
H. Chen, R. Tao, H. Zhang, Y. Wang, W. Ye, J. Wang, G. Hu, and M. Savvides, · 2022
Later among the works it cites.
“Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation,”
Y. Ji, H. Bai, C. GE, J. Yang, Y. Zhu, R. Zhang, Z. Li, L. Zhanng, W. Ma, X. Wan, and P. Luo, · 2022
Later among the works it cites.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A.C. Berg, W.Y. Lo, P. Dollár, and R. Girshick, · 2023
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T. Zhou, S. Wang, Y. Zhou, Y. Yao, J. Li, and L. Shao, · 2020
Cited alongside, same era.
“Cholecseg8k: A semantic segmentation dataset for laparoscopic cholecystectomy based on cholec80,”
W.Y. Hong, C.L. Kao, Y.H. Kuo, J.R. Wang, W.L. Chang, and C.S. Shih, · 2020
Cited alongside, same era.
“Full-duplex strategy for video object segmentation,”
G.P. Ji, K. Fu, Z. Wu, D.P. Fan, J. Shen, and L. Shao, · 2021
Cited alongside, same era.
J. Yang, M. Gao, Z. Li, S. Gao, F. Wang, and F. Zheng, · 2023
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“Segment anything in medical images,”
J. Ma, Y. He, F. Li, L. Han, C. You, and B. Wang, · 2024
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“Segment anything model for medical images?,”
Y. Huang, X. Yang, L. Liu, H. Zhou, A. Chang, X. Zhou, R. Chen, J. Yu, J. Chen, C. Chen, et al., · 2024
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