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Automatic segmentation of medical images is crucial in modern clinical workflows.
A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI,
C. Li, R. Huang, Z. Ding, J. C. Gatenby, D. N. Metaxas, and J. C. Gore, · 2011
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Automatic tuberculosis screening using chest radiographs,
S. Jaeger et al., · 2013
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U-Net: Convolutional Networks for Biomedical Image Segmentation,
O. Ronneberger, P. Fischer, and T. Brox, · 2015
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Deep learning in medical imaging and radiation therapy,
B. Sahiner, A. Pezeshk, L. M. Hadjiiski, X. Wang, K. Drukker, K. H. Cha, R. M. Summers, and M. L. Giger, · 2019
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Unet++: Redesigning skip connections to exploit multiscale features in image segmentation,
Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, · 2019
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Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge,
X. Zhuang et al., · 2019
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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II
A. Crimi, S. Bakas, H. Kuijf, F. Keyvan, M. Reyes, and T. van Walsum, · 2019
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nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation,
F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. H. Maier-Hein, · 2020
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Deep distance transform for tubular structure segmentation in ct scans,
Y. Wang, X. Wei, F. Liu, J. Chen, Y. Zhou, W. Shen, E. K. Fishman, and A. L. Yuille, · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale,
A. Dosovitskiy et al., · 2020
Cited alongside, same era.
Learning Euler’s elastica model for medical image segmentation,
X. Chen, X. Luo, Y. Zhao, S. Zhang, G. Wang, and Y. Zheng, · 2020
Cited alongside, same era.
A review of deep-learning-based medical image segmentation methods,
X. Liu, L. Song, S. Liu, and Y. Zhang, · 2021
Cited alongside, same era.
Gt u-net: A u-net like group transformer network for tooth root segmentation,
Y. Li, S. Wang, J. Wang, G. Zeng, W. Liu, Q. Zhang, Q. Jin, and Y. Wang, · 2021
Cited alongside, same era.
U-net and its variants for medical image segmentation: A review of theory and applications,
N. Siddique, S. Paheding, C. P. Elkin, and V. Devabhaktuni, · 2021
Cited alongside, same era.
Tinyvit: Fast pretraining distillation for small vision transformers,
K. Wu, J. Zhang, H. Peng, M. Liu, B. Xiao, J. Fu, and L. Yuan, · 2022
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Attention U-Net: Learning Where to Look for the Pancreas,
O. Oktay et al., · 2022
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Lvit: language meets vision transformer in medical image segmentation,
Z. Li, Y. Li, Q. Li, P. Wang, D. Guo, L. Lu, D. Jin, Y. Zhang, and Q. Hong, · 2023
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A. Kirillov et al., · 2023
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Faster Segment Anything: Towards Lightweight SAM for Mobile Applications,
C. Zhang, D. Han, Y. Qiao, J. U. Kim, S.-H. Bae, S. Lee, and C. S. Hong, · 2023
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TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation,
J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A. L. Yuille, and Y. Zhou, · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Semi-supervised medical image segmentation through dual-task consistency,
X. Luo, J. Chen, T. Song, and G. Wang, · 2021
Cited alongside, same era.
Agmb-transformer: Anatomy-guided multi-branch transformer network for automated evaluation of root canal therapy,
Y. Li et al., · 2021
Cited alongside, same era.
M. A. Mazurowski, H. Dong, H. Gu, J. Yang, N. Konz, and Y. Zhang, · 2023
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Segment Anything in Medical Images,
J. Ma, Y. He, F. Li, L. Han, C. You, and B. Wang, · 2023
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AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder,
T. Shaharabany, A. Dahan, R. Giryes, and L. Wolf, · 2023
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AMSC-Net: Anatomy and multi-label semantic consistency network for semi-supervised fluid segmentation in retinal OCT,
Y. Wang, R. Dan, S. Luo, L. Sun, Q. Wu, Y. Li, X. Chen, K. Yan, X. Ye, and D. Yu, · 2024
Closest in time.