D-Former: A U-shaped Dilated Transformer for 3D Medical Image Segmentation

Yi-de, M., Qing, L., Zhi-Bai, Q.: Automated image segmentation using improved PCNN model based on cross-entropy. In: International Symposium on Intelligent Multimedia, Video and Speech Processing (2004)

2004

Earlier work this paper cites.

Deng, J., Dong, W., Socher, R., et al

2009

Earlier work this paper cites.

Bottou, L.: Stochastic gradient descent tricks. In: Neural Networks: Tricks of the Trade, (2012)

Original

2012

Earlier work this paper cites.

Chen, L.-C., Papandreou, G., Kokkinos, I., et al.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. ArXiv Preprint ArXiv:1412.7062 (2014)

Original

2014

Earlier work this paper cites.

Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. ArXiv Preprint ArXiv:1511.07122 (2015)

Original

2015

Earlier work this paper cites.

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)

2015

Earlier work this paper cites.

Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: MICCAI (2015)

2015

Earlier work this paper cites.

Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., et al

2016

Earlier work this paper cites.

Korez, R., Likar, B., Pernuš, F., et al

2016

Earlier work this paper cites.

Zhou, X., Ito, T., Takayama, R., et al

2016

Earlier work this paper cites.

Moeskops, P., Wolterink, J.M., et al

2016

Earlier work this paper cites.

Shakeri, M., Tsogkas, S., Ferrante, E., et al

2016

Earlier work this paper cites.

Alansary, A., Kamnitsas, K., Davidson, A., et al

2016

Earlier work this paper cites.

Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., et al

2016

Earlier work this paper cites.

Drozdzal, M., Vorontsov, E., Chartrand, G., et al

2016

Earlier work this paper cites.

Ghafoorian, M., Karssemeijer, N., Heskes, T., et al

2016

Earlier work this paper cites.

Brosch, T., Tang, L.Y., Yoo, Y., et al.: Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. TMI (2016)

2016

Earlier work this paper cites.

Brosch, T., Tang, L.Y., Yoo, Y., et al.: Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. TMI (2016)

2016

Earlier work this paper cites.

Pereira, S., Pinto, A., et al.: Brain tumor segmentation using convolutional neural networks in MRI images. TMI (2016)

2016

Earlier work this paper cites.

Milletari, F., Navab, N., Ahmadi, S.-A.: V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In: 3DV (2016)

2016

Earlier work this paper cites.

Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. ArXiv Preprint ArXiv:1607.06450 (2016)

Original

2016

Earlier work this paper cites.

Peng, C., Zhang, X., Yu, G., et al

2017

Earlier work this paper cites.

Chen, L.-C., Papandreou, G., Kokkinos, I., et al.: DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. PAMI (2017)

2017

Earlier work this paper cites.

Zhao, H., Shi, J., Qi, X., et al

2017

Earlier work this paper cites.

Dai, J., Qi, H., Xiong, Y., et al

2017

Earlier work this paper cites.

Kauderer-Abrams, E.: Quantifying translation-invariance in convolutional neural networks. ArXiv Preprint ArXiv:1801.01450 (2017)

Original

2017

Earlier work this paper cites.

Vaswani, A., Shazeer, N., Parmar, N., et al

2017

Earlier work this paper cites.

Kamnitsas, K., Ledig, C., Newcombe, V.F., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. MIA (2017)

2017

Earlier work this paper cites.

Christ, P.F., Ettlinger, F., et al.: Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. ArXiv Preprint ArXiv:1702.05970 (2017)

Original

2017

Cited alongside, same era.

Chen, L.-C., Papandreou, G., Kokkinos, I., et al.: DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. TPAMI (2017)

2017

Cited alongside, same era.

Chen, L.-C., Papandreou, G., Schroff, F., et al.: Rethinking atrous convolution for semantic image segmentation. ArXiv Preprint ArXiv:1706.05587 (2017)

Original

2017

Cited alongside, same era.

Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: CVPR, pp. 1251–1258 (2017)

2017

Cited alongside, same era.

Chen, L.-C., Zhu, Y., Papandreou, G., et al

2018

Zhu, X., Su, W., Lu, L., et al.: Deformable DETR: Deformable Transformers for end-to-end object detection. ArXiv Preprint ArXiv:2010.04159 (2020)

Original

2020

Later among the works it cites.

Wu, Z., Liu, Z., et al.: Lite Transformer with long-short range attention. ArXiv Preprint ArXiv:2004.11886 (2020)

Original

2020

Later among the works it cites.

Cai, S., Tian, Y., Lui, H., et al.: Dense-UNet: A novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network. Quantitative Imaging in Medicine and Surgery (2020)

2020

Later among the works it cites.

Chen, J., Lu, Y., Yu, Q., et al.: TransUNet: Transformers make strong encoders for medical image segmentation. ArXiv Preprint ArXiv:2102.04306 (2021)

Original

2021

Later among the works it cites.

Sun, Z., Cao, S., Yang, Y., et al