Built on

R. Keys, “Cubic convolution interpolation for digital image processing,”
1981
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G. R. Arce, J. Bacca, and J. L. Paredes, “3.2 - nonlinear filtering for image analysis and enhancement,” in
2005
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M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in
2010
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M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in
2014
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D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in
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2015
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J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in
2015
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O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in
2015
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Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” in
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
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2016
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W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in
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2017
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I. Loshchilov and F. Hutter, “SGDR: stochastic gradient descent with warm restarts,” in
2017
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, u. Kaiser, and I. Polosukhin, “Attention is all you need,” in
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C. F. Baumgartner, L. M. Koch, M. Pollefeys, and E. Konukoglu, “An exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation,” in
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F. Isensee, J. Petersen, A. Klein, D. Zimmerer, P. F. Jaeger, S. Kohl, J. Wasserthal, G. Koehler, T. Norajitra, S. Wirkert, and K. H. Maier-Hein, “nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation,”
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2018
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S. Liu, D. Xu, S. K. Zhou, O. Pauly, S. Grbic, T. Mertelmeier, J. Wicklein, A. Jerebko, W. Cai, and D. Comaniciu, “3d anisotropic hybrid network: Transferring convolutional features from 2d images to 3d anisotropic volumes,” in
2018
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O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y. Hammerla, B. Kainz
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2018
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X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in
2018
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Reading the bibliography…

2021

More than Encoder: Introducing Transformer Decoder to Upsample

Li, Yijiang, Cai, Wentian, Gao, Ying et al.

Understand

Medical image segmentation methods downsample images for feature extraction and then upsample them to restore resolution for pixel-level predictions.

In such a schema, upsample technique is vital in restoring information for better performance.
However, existing upsample techniques leverage little information from downsampling paths.
The local and detailed feature from the shallower layer such as boundary and tissue texture is particularly more important in medical segmentation compared with natural image segmentation.

Built on

R. Keys, “Cubic convolution interpolation for digital image processing,”
1981
Earlier work this paper cites.
G. R. Arce, J. Bacca, and J. L. Paredes, “3.2 - nonlinear filtering for image analysis and enhancement,” in
2005
Earlier work this paper cites.
M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in
2010
Earlier work this paper cites.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in
Original
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in
2015
Earlier work this paper cites.
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” in
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
Earlier work this paper cites.
F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in
2016
Earlier work this paper cites.
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in
2016
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam, “Rethinking atrous convolution for semantic image segmentation,”
Original
2017
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “SGDR: stochastic gradient descent with warm restarts,” in
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, u. Kaiser, and I. Polosukhin, “Attention is all you need,” in
2017
Earlier work this paper cites.
C. F. Baumgartner, L. M. Koch, M. Pollefeys, and E. Konukoglu, “An exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation,” in
2018
Earlier work this paper cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in
2018
Earlier work this paper cites.
F. Isensee, J. Petersen, A. Klein, D. Zimmerer, P. F. Jaeger, S. Kohl, J. Wasserthal, G. Koehler, T. Norajitra, S. Wirkert, and K. H. Maier-Hein, “nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation,”
Original
2018
Earlier work this paper cites.
S. Liu, D. Xu, S. K. Zhou, O. Pauly, S. Grbic, T. Mertelmeier, J. Wicklein, A. Jerebko, W. Cai, and D. Comaniciu, “3d anisotropic hybrid network: Transferring convolutional features from 2d images to 3d anisotropic volumes,” in
2018
Earlier work this paper cites.
A. Myronenko, “3d mri brain tumor segmentation using autoencoder regularization,” in
2018
Earlier work this paper cites.
O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y. Hammerla, B. Kainz
Original
2018
Earlier work this paper cites.
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in
2018
Earlier work this paper cites.

Similar

Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++: A nested u-net architecture for medical image segmentation,” in
2018
Cited alongside, same era.
Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “UNet++: A Nested U-Net Architecture for Medical Image Segmentation,”
Original
2018
Cited alongside, same era.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in
2019
Cited alongside, same era.
X. He, S. Yang, G. Li, H. Li, H. Chang, and Y. Yu, “Non-local context encoder: Robust biomedical image segmentation against adversarial attacks,” in
2019
Cited alongside, same era.
J. Ho, N. Kalchbrenner, D. Weissenborn, and T. Salimans, “Axial attention in multidimensional transformers,”
Original

Then

Y. He, D. Yang, H. Roth, C. Zhao, and D. Xu, “Dints: Differentiable neural network topology search for 3d medical image segmentation,” in
2021
Closest in time.
D. Karimi, S. Vasylechko, and A. Gholipour, “Convolution-Free Medical Image Segmentation using Transformers,”
Original
2021
Closest in time.
S. Li, X. Sui, X. Luo, X. Xu, Y. Liu, and R. Goh, “Medical image segmentation using squeeze-and-expansion transformers,”
Original
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Closest in time.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,”
Original
2021
Closest in time.
O. Petit, N. Thome, C. Rambour, and L. Soler, “U-Net Transformer: Self and Cross Attention for Medical Image Segmentation,”

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

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