SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation

Kalman, R.E.: A new approach to linear filtering and prediction problems (1960)

1960

Earlier work this paper cites.

Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34

2014

Earlier work this paper cites.

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data 4

2017

Earlier work this paper cites.

Granados-Romero, J.J., Valderrama-Treviño, A.I., Contreras-Flores, E.H., Barrera-Mera, B., Herrera Enríquez, M., Uriarte-Ruíz, K., Ceballos-Villalba, J.C., Estrada-Mata, A.G., Alvarado Rodríguez, C., Arauz-Peña, G.: Colorectal cancer: a review. Int J Res Med Sci 5

2017

Earlier work this paper cites.

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

2017

Earlier work this paper cites.

Myronenko, A.: 3d mri brain tumor segmentation using autoencoder regularization. In: International MICCAI Brainlesion Workshop. pp. 311–320. Springer (2018)

2018

Earlier work this paper cites.

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., 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.

Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18

2021

Earlier work this paper cites.

Liu, H., Dai, Z., So, D., Le, Q.V.: Pay attention to mlps. Advances in Neural Information Processing Systems 34

2021

Earlier work this paper cites.

Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10012–10022 (2021)

2021

Earlier work this paper cites.

Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop. pp. 272–284. Springer (2022)

2022

Cited alongside, same era.

Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 574–584 (2022)

2022

Cited alongside, same era.

Lee, H.H., Bao, S., Huo, Y., Landman, B.A.: 3d ux-net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation. arXiv preprint arXiv:2209.15076 (2022)

Original

2022

Cited alongside, same era.

Xing, Z., Yu, L., Wan, L., Han, T., Zhu, L.: Nestedformer: Nested modality-aware transformer for brain tumor segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 140–150. Springer (2022)

Roy, S., Koehler, G., Ulrich, C., Baumgartner, M., Petersen, J., Isensee, F., Jaeger, P.F., Maier-Hein, K.H.: Mednext: transformer-driven scaling of convnets for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 405–415. Springer (2023)

2023

Later among the works it cites.

Wang, H., Zhu, L., Yang, G., Guo, Y., Zhang, S., Xu, B., Jin, Y.: Video-instrument synergistic network for referring video instrument segmentation in robotic surgery. arXiv preprint arXiv:2308.09475 (2023)

Original

2023

Later among the works it cites.

Xing, Z., Wan, L., Fu, H., Yang, G., Zhu, L.: Diff-unet: A diffusion embedded network for volumetric segmentation. arXiv preprint arXiv:2303.10326 (2023)

Original

2023

Later among the works it cites.

Zhao, J., Xing, Z., Chen, Z., Wan, L., Han, T., Fu, H., Zhu, L.: Uncertainty-aware multi-dimensional mutual learning for brain and brain tumor segmentation. IEEE Journal of Biomedical and Health Informatics (2023)