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Image segmentation remains a pivotal component in medical image analysis, aiding in the extraction of critical information for precise diagnostic practices.
“U-Net: Convolutional Networks for Biomedical Image Segmentation,”
Olaf Ronneberger, Philipp Fischer, and Thomas Brox, · 2015
Earlier work this paper cites.
“Multi-Atlas Labeling Beyond the Cranial Vault - Workshop and Challenge,”
Bennett Landman, Zhoubing Xu, J Igelsias, Martin Styner, T Langerak, and Arno Klein, · 2015
Earlier work this paper cites.
“Deep Residual Learning for Image Recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Earlier work this paper cites.
“Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: Is the problem solved?,”
Olivier Bernard, Alain Lalande, et al., · 2018
Earlier work this paper cites.
Amber L. Simpson, Michela Antonelli, et al., · 2019
Earlier work this paper cites.
“An image is worth 16x16 words: Transformers for image recognition at scale,”
Alexey Dosovitskiy, Lucas Beyer, et al., · 2020
Earlier work this paper cites.
“nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation,”
Fabian Isensee, Paul Jaeger, Simon Kohl, Jens Petersen, and Klaus Maier-Hein, · 2021
Earlier work this paper cites.
“TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation,”
Chen, Jieneng and Lu, Yongyi and Yu, Qihang and Luo, Xiangde and Adeli, Ehsan and Wang, Yan and Lu, Le and Yuille, Alan L., and Zhou, Yuyin, · 2021
Cited alongside, same era.
“MISSFormer: An Effective Medical Image Segmentation Transformer,”
Xiaohong Huang, Zhifang Deng, Dandan Li, and Xueguang Yuan, · 2021
Cited alongside, same era.
“nnFormer: Interleaved Transformer for Volumetric Segmentation,”
Hong-Yu Zhou, Jiansen Guo, Zhang Yinghao, Lequan Yu, Liansheng Wang, and Yizhou Yu, · 2021
Cited alongside, same era.
“Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation,”
Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang, · 2022
Cited alongside, same era.
“UNETR: Transformers for 3D Medical Image Segmentation,”
“Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images,”
Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger Roth, and Daguang Xu, · 2022
Later among the works it cites.
“UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation,”
Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, and Fahad Shahbaz Khan, · 2022
Later among the works it cites.
“Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation,”
Moein Heidari, Amirhossein Kazerouni, Milad Soltany, Reza Azad, Ehsan Khodapanah Aghdam, Julien Cohen-Adad, and Dorit Merhof, · 2023
Closest in time.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick, · 2023
Closest in time.
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A. Hatamizadeh, Y. Tang, V. Nath, D. Yang, A. Myronenko, B. Landman, H. R. Roth, and D. Xu, · 2022
Cited alongside, same era.
“TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation,”
Reza Azad, Moein Heidari, Moein Shariatnia, Ehsan Khodapanah Aghdam, Sanaz Karimijafarbigloo, Ehsan Adeli, and Dorit Merhof, · 2022
Cited alongside, same era.
Kaidong Zhang and Dong Liu, · 2023
Closest in time.
“Segment anything in medical images,”
Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang, · 2023
Closest in time.