Integrating Mamba Sequence Model and Hierarchical Upsampling Network for Accurate Semantic Segmentation of Multiple Sclerosis Legion

A. Tustin, “A method of analysing the behaviour of linear systems in terms of time series,” Journal of the Institution of Electrical Engineers-Part IIA: Automatic Regulators and Servo Mechanisms , vol. 94, no. 1, pp. 130–142, 1947

1947

Earlier work this paper cites.

Y. LeCun, Y. Bengio et al. , “Convolutional networks for images, speech, and time series,” The handbook of brain theory and neural networks , vol. 3361, no. 10, p. 1995, 1995

1995

Earlier work this paper cites.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440

2015

Earlier work this paper cites.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 . Springer, 2015, pp. 234–241

2015

Earlier work this paper cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need in advances in neural information processing systems, 2017,” Search PubMed , pp. 5998–6008

2017

Earlier work this paper cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017

2017

Earlier work this paper cites.

X. Li, H. Chen, X. Qi, Q. Dou, C.-W. Fu, and P.-A. Heng, “H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes,” IEEE transactions on medical imaging , vol. 37, no. 12, pp. 2663–2674, 2018

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 et al. , “Attention u-net: Learning where to look for the pancreas,” arXiv preprint arXiv:1804.03999 , 2018

Original

2018

Earlier work this paper cites.

Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++: Redesigning skip connections to exploit multiscale features in image segmentation,” IEEE transactions on medical imaging , vol. 39, no. 6, pp. 1856–1867, 2019

2019

Earlier work this paper cites.

N. Ibtehaz and M. S. Rahman, “Multiresunet: Rethinking the u-net architecture for multimodal biomedical image segmentation,” Neural networks , vol. 121, pp. 74–87, 2020

2020

Earlier work this paper cites.

Y. Zhang, L. Yuan, Y. Wang, and J. Zhang, “Sau-net: efficient 3d spine mri segmentation using inter-slice attention,” in Medical Imaging with Deep Learning . PMLR, 2020, pp. 903–913

2020

Earlier work this paper cites.

A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929 , 2020

Original

2020

Earlier work this paper cites.

A. Gu, T. Dao, S. Ermon, A. Rudra, and C. Ré, “Hippo: Recurrent memory with optimal polynomial projections,” Advances in neural information processing systems , vol. 33, pp. 1474–1487, 2020

2020

Earlier work this paper cites.

Y. Tay, M. Dehghani, S. Abnar, Y. Shen, D. Bahri, P. Pham, J. Rao, L. Yang, S. Ruder, and D. Metzler, “Long range arena: A benchmark for efficient transformers,” arXiv preprint arXiv:2011.04006 , 2020

Original

2020

Cited alongside, same era.

N. Heller, F. Isensee, K. H. Maier-Hein, X. Hou, C. Xie, F. Li, Y. Nan, G. Mu, Z. Lin, M. Han et al. , “The state of the art in kidney and kidney tumor segmentation in contrast-enhanced ct imaging: Results of the kits19 challenge,” Medical image analysis , vol. 67, p. 101821, 2021

2021

Cited alongside, same era.

Z. Wang, Z. Zhang, and I. Voiculescu, “Rar-u-net: a residual encoder to attention decoder by residual connections framework for spine segmentation under noisy labels,” in 2021 IEEE International Conference on Image Processing (ICIP) . IEEE, 2021, pp. 21–25

2021

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,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 10 012–10 022

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, pp. 574–584

2022

Later among the works it cites.

H. Mehta, A. Gupta, A. Cutkosky, and B. Neyshabur, “Long range language modeling via gated state spaces,” arXiv preprint arXiv:2206.13947 , 2022

Original

2022

Later among the works it cites.

A. M. Muslim, S. Mashohor, G. Al Gawwam, R. Mahmud, M. binti Hanafi, O. Alnuaimi, R. Josephine, and A. D. Almutairi, “Brain mri dataset of multiple sclerosis with consensus manual lesion segmentation and patient meta information,” Data in Brief , vol. 42, p. 108139, 2022

2022

Later among the works it cites.

P. Bilic, P. Christ, H. B. Li, E. Vorontsov, A. Ben-Cohen, G. Kaissis, A. Szeskin, C. Jacobs, G. E. H. Mamani, G. Chartrand et al. , “The liver tumor segmentation benchmark (lits),” Medical Image Analysis , vol. 84, p. 102680, 2023