VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation

Ronneberger, O., Fischer, P., Brox, T.: 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. pp. 234–241. Springer (2015)

2015

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.

Cheng, J.Z., Ni, D., Chou, Y.H., Qin, J., Tiu, C.M., Chang, Y.C., Huang, C.S., Shen, D., Chen, C.M.: Computer-aided diagnosis with deep learning architecture: applications to breast lesions in us images and pulmonary nodules in ct scans. Scientific reports 6

2016

Earlier work this paper cites.

Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19. pp. 424–432. Springer (2016)

2016

Earlier work this paper cites.

Golan, R., Jacob, C., Denzinger, J.: Lung nodule detection in ct images using deep convolutional neural networks. In: 2016 international joint conference on neural networks (IJCNN). pp. 243–250. IEEE (2016)

2016

Earlier work this paper cites.

Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

Original

2017

Earlier work this paper cites.

Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV). pp. 801–818 (2018)

2018

Earlier work this paper cites.

Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8759–8768 (2018)

2018

Earlier work this paper cites.

Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., 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.

Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). pp. 3–19 (2018)

2018

Cited alongside, same era.

Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4. pp. 3–11. Springer (2018)

2018

Cited alongside, same era.

Alom, M.Z., Yakopcic, C., Hasan, M., Taha, T.M., Asari, V.K.: Recurrent residual u-net for medical image segmentation. Journal of Medical Imaging 6

2019

Cited alongside, same era.

Shazeer, N.: Glu variants improve transformer. arXiv preprint arXiv:2002.05202 (2020)

Original

2020

Azad, R., Al-Antary, M.T., Heidari, M., Merhof, D.: Transnorm: Transformer provides a strong spatial normalization mechanism for a deep segmentation model. IEEE Access 10

2022

Later among the works it cites.

Azad, R., Heidari, M., Shariatnia, M., Aghdam, E.K., Karimijafarbigloo, S., Adeli, E., Merhof, D.: Transdeeplab: Convolution-free transformer-based deeplab v3+ for medical image segmentation. In: International Workshop on PRedictive Intelligence In MEdicine. pp. 91–102. Springer (2022)

2022

Later among the works it cites.

Gao, Y., Zhou, M., Liu, D., Metaxas, D.: A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:2203.00131 (2022)

Original

2022

Later among the works it cites.

Ruan, J., Xiang, S., Xie, M., Liu, T., Fu, Y.: Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). pp. 1150–1156. IEEE (2022)

2022