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Deep learning models have become the mainstream method for medical image segmentation, but they require a large manually labeled dataset for training and are difficult to extend to unseen categories.
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F. F. Li, Member, IEEE, R. Fergus, and S. Member, · 2006
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“U-net: Convolutional networks for biomedical image segmentation,”
Olaf Ronneberger, Philipp Fischer, and Thomas Brox, · 2015
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“2015 MICCAI Multi-Atlas Labeling Beyond the Cranial Vault – Workshop and Challenge,”
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“Matching networks for one shot learning,”
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“Prototypical networks for few-shot learning,” 2017
Jake Snell, Kevin Swersky, and Richard S. Zemel, · 2017
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“Learning to compare: Relation network for few-shot learning,”
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H. S. Torr, and Timothy M. Hospedales, · 2017
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“Few-shot learning with graph neural networks,”
V. Garcia and J. Bruna, · 2017
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“Cnn-based segmentation of medical imaging data,”
B. Kayalibay, G. Jensen, and Vds Patrick, · 2017
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“Unet++: A nested u-net architecture for medical image segmentation,”
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang, · 2018
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“Attention u-net: Learning where to look for the pancreas,”
O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. Mcdonagh, N. Y. Hammerla, and B. Kainz, · 2018
Cited alongside, same era.
“Transunet: Transformers make strong encoders for medical image segmentation,”
J. Chen, Y. Lu, Q. Yu, X. Luo, and Y. Zhou, · 2021
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“Swin-unet: Unet-like pure transformer for medical image segmentation,”
H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M. Wang, · 2021
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“Adaptive prototype learning and allocation for few-shot segmentation,”
Gen Li, Varun Jampani, Laura Sevilla-Lara, Deqing Sun, Jonghyun Kim, and Joongkyu Kim, · 2021
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“A location-sensitive local prototype network for few-shot medical image segmentation,”
Q. Yu, K. Dang, N. Tajbakhsh, D. Terzopoulos, and X. Ding, · 2021
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“Recurrent mask refinement for few-shot medical image segmentation,”
Hao Tang, Xingwei Liu, Shanlin Sun, Xiangyi Yan, and Xiaohui Xie, · 2021
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“Automated design of deep learning methods for biomedical image segmentation,”
F. Isensee, P. F. Jger, Saa Kohl, J. Petersen, and K. H. Maier-Hein, · 2019
Cited alongside, same era.
“Panet: Few-shot image semantic segmentation with prototype alignment,”
Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, and Jiashi Feng, · 2019
Cited alongside, same era.
“Part-aware prototype network for few-shot semantic segmentation,”
Yongfei Liu, Xiangyi Zhang, Songyang Zhang, and Xuming He, · 2020
Cited alongside, same era.
“Self-supervision with superpixels: Training few-shot medical image segmentation without annotation,”
Cheng Ouyang, Carlo Biffi, Chen Chen, Turkay Kart, Huaqi Qiu, and Daniel Rueckert, · 2020
Cited alongside, same era.
“Crnet: Cross-reference networks for few-shot segmentation,”
W. Liu, C. Zhang, G. Lin, and F. Liu, · 2020
Cited alongside, same era.
“’squeeze & excite’ guided few-shot segmentation of volumetric images,”
A. G. Roy, S. Siddiqui, Sebastian Plsterl, N. Navab, and C. Wachinger,
Cited in the paper.
A. E. Kavur, N. S. Gezer, M. Bar, S. Aslan, P. H. Conze, V. Groza, D. D. Pham, S. Chatterjee, P. Ernst, and S. Zkan, · 2021
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“Unext: Mlp-based rapid medical image segmentation network,”
Jeya Maria Jose Valanarasu and Vishal M. Patel, · 2022
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“Self-support few-shot semantic segmentation,”
Qi Fan, Wenjie Pei, Yu-Wing Tai, and Chi-Keung Tang, · 2022
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“Learning non-target knowledge for few-shot semantic segmentation,”
Yuanwei Liu, Nian Liu, Qinglong Cao, Xiwen Yao, Junwei Han, and Ling Shao, · 2022
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“Few-shot medical image segmentation with cycle-resemblance attention,”
Hao Ding, Changchang Sun, Hao Tang, Dawen Cai, and Yan Yan, · 2023
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