Built on

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE
1998
Earlier work this paper cites.
B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, et al
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention
2015
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2015
Earlier work this paper cites.
B. Landman, Z. Xu, J. E. Igelsias, M. Styner, T. Langerak, and A. Klein, “Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,” in Proc. MICCAI: Multi-Atlas Labeling Beyond Cranial Vault-Workshop Challenge
2015
Earlier work this paper cites.
D. Hendrycks and K. Gimpel, “Gaussian error linear units (gelus),” arXiv preprint arXiv:1606.08415
Original
2016
Earlier work this paper cites.
J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” arXiv preprint arXiv:1607.06450
Original
2016
Earlier work this paper cites.
W. Luo, Y. Li, R. Urtasun, and R. Zemel, “Understanding the effective receptive field in deep convolutional neural networks,” in Proceedings of the 30th International Conference on Neural Information Processing Systems
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al
2017
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2017
Earlier work this paper cites.
S. Liu, D. Huang, et al
2018
Earlier work this paper cites.
O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, X. Yang, P.-A. Heng, I. Cetin, K. Lekadir, O. Camara, M. A. G. Ballester, et al
2018
Earlier work this paper cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, et al
Original
2020
Earlier work this paper cites.

Similar

N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European Conference on Computer Vision
2020
Cited alongside, same era.
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,” arXiv preprint arXiv:2103.14030
Original
2021
Cited alongside, same era.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” arXiv preprint arXiv:2111.06377
Original
2021
Cited alongside, same era.
H.-Y. Zhou, C. Lu, S. Yang, and Y. Yu, “ConvNets vs. Transformers: Whose visual representations are more transferable?,” ICCV Workshop on Deep Multi-Task Learning in Computer Vision
2021
Cited alongside, same era.

Then

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2021

nnFormer: Interleaved Transformer for Volumetric Segmentation

Zhou, Hong-Yu, Guo, Jiansen, Zhang, Yinghao et al.

Understand

Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community.

Given the ability to exploit long-term dependencies, transformers are promising to help atypical convolutional neural networks to overcome their inherent shortcomings of spatial inductive bias.
However, most of recently proposed transformer-based segmentation approaches simply treated transformers as assisted modules to help encode global context into convolutional representations.
To address this issue, we introduce nnFormer, a 3D transformer for volumetric medical image segmentation.

Built on

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE
1998
Earlier work this paper cites.
B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, et al
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention
2015
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2015
Earlier work this paper cites.
B. Landman, Z. Xu, J. E. Igelsias, M. Styner, T. Langerak, and A. Klein, “Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,” in Proc. MICCAI: Multi-Atlas Labeling Beyond Cranial Vault-Workshop Challenge
2015
Earlier work this paper cites.
D. Hendrycks and K. Gimpel, “Gaussian error linear units (gelus),” arXiv preprint arXiv:1606.08415
Original
2016
Earlier work this paper cites.
J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” arXiv preprint arXiv:1607.06450
Original
2016
Earlier work this paper cites.
W. Luo, Y. Li, R. Urtasun, and R. Zemel, “Understanding the effective receptive field in deep convolutional neural networks,” in Proceedings of the 30th International Conference on Neural Information Processing Systems
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al
2017
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2017
Earlier work this paper cites.
S. Liu, D. Huang, et al
2018
Earlier work this paper cites.
O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, X. Yang, P.-A. Heng, I. Cetin, K. Lekadir, O. Camara, M. A. G. Ballester, et al
2018
Earlier work this paper cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, et al
Original
2020
Earlier work this paper cites.

Similar

N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European Conference on Computer Vision
2020
Cited alongside, same era.
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,” arXiv preprint arXiv:2103.14030
Original
2021
Cited alongside, same era.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” arXiv preprint arXiv:2111.06377
Original
2021
Cited alongside, same era.
H.-Y. Zhou, C. Lu, S. Yang, and Y. Yu, “ConvNets vs. Transformers: Whose visual representations are more transferable?,” ICCV Workshop on Deep Multi-Task Learning in Computer Vision
2021
Cited alongside, same era.

Then

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

D. Zhang, G. Huang, Q. Zhang, J. Han, J. Han, and Y. Yu, “Cross-modality deep feature learning for brain tumor segmentation,” Pattern Recognition

2021

Cited alongside, same era.

S. Tuli, I. Dasgupta, E. Grant, and T. L. Griffiths, “Are convolutional neural networks or transformers more like human vision?,” arXiv preprint arXiv:2105.07197

Original

2021

Cited alongside, same era.

J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, et al

Original

2021

Cited alongside, same era.

Y. Zhang, H. Liu, and Q. Hu, “TransFuse: Fusing transformers and cnns for medical image segmentation,” arXiv preprint arXiv:2102.08005

Original

2021

Cited alongside, same era.

J. M. J. Valanarasu, P. Oza, I. Hacihaliloglu, and V. M. Patel, “Medical Transformer: Gated axial-attention for medical image segmentation,” arXiv preprint arXiv:2102.10662

Original

2021

Cited alongside, same era.

Y. Chang, H. Menghan, Z. Guangtao, and Z. Xiao-Ping, “TransClaw U-Net: Claw u-net with transformers for medical image segmentation,” arXiv preprint arXiv:2107.05188

Original

2021

Cited alongside, same era.

B. Chen, Y. Liu, Z. Zhang, G. Lu, and D. Zhang, “TransAttUnet: Multi-level attention-guided u-net with transformer for medical image segmentation,” arXiv preprint arXiv:2107.05274

Original

2021

Cited alongside, same era.

D. Karimi, S. Vasylechko, and A. Gholipour, “Convolution-free medical image segmentation using transformers,” arXiv preprint arXiv:2102.13645

Original

2021

Cited alongside, same era.

Original

2021

Closest in time.

Y. Xie, J. Zhang, C. Shen, and Y. Xia, “CoTr: Efficiently bridging cnn and transformer for 3d medical image segmentation,” arXiv preprint arXiv:2103.03024

Original

2021

Closest in time.

W. Wang, C. Chen, M. Ding, J. Li, H. Yu, and S. Zha, “TransBTS: Multimodal brain tumor segmentation using transformer,” arXiv preprint arXiv:2103.04430

Original

2021

Closest in time.

S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, P. H. Torr, et al

2021

Closest in time.

X. Huang, Z. Deng, D. Li, and X. Yuan, “MISSFormer: An effective medical image segmentation transformer,” arXiv preprint arXiv:2109.07162

Original

2021

Closest in time.

M. Antonelli, A. Reinke, S. Bakas, K. Farahani, B. A. Landman, G. Litjens, B. Menze, O. Ronneberger, R. M. Summers, B. van Ginneken, et al

Original

2021

Closest in time.

F. Isensee, P. F. Jaeger, S. A. Kohl, J. Petersen, and K. H. Maier-Hein, “nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,” Nature methods

2021

Closest in time.

T. Qu, X. Wang, C. Fang, L. Mao, J. Li, P. Li, J. Qu, X. Li, H. Xue, Y. Yu, et al

2022

Closest in time.

A. Hatamizadeh, Y. Tang, V. Nath, D. Yang, A. Myronenko, B. Landman, H. R. Roth, and D. Xu, “UNETR: Transformers for 3d medical image segmentation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision

2022

Closest in time.