Transformer based Generative Adversarial Network for Liver Segmentation

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in neural information processing systems 27

2014

Earlier work this paper cites.

Luc, P., Couprie, C., Chintala, S., Verbeek, J.: Semantic Segmentation using Adversarial Networks. In: NIPS Workshop on Adversarial Training. Barcelona, Spain (Dec 2016), https://hal.inria.fr/hal-01398049

2016

Earlier work this paper cites.

Cornelis, F., Martin, M., Saut, O., Buy, X., Kind, M., Palussiere, J., Colin, T.: Precision of manual two-dimensional segmentations of lung and liver metastases and its impact on tumour response assessment using recist 1.1. European radiology experimental 1

2017

Earlier work this paper cites.

Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. CVPR (2017)

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.

Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision. pp. 2223–2232 (2017)

2017

Earlier work this paper cites.

Chlebus, G., Schenk, A., Moltz, J.H., van Ginneken, B., Hahn, H.K., Meine, H.: Automatic liver tumor segmentation in ct with fully convolutional neural networks and object-based postprocessing. Scientific reports 8

2018

Cited alongside, same era.

Chuquicusma, M.J., Hussein, S., Burt, J., Bagci, U.: How to fool radiologists with generative adversarial networks? a visual turing test for lung cancer diagnosis. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). pp. 240–244. IEEE (2018)

2018

Cited alongside, same era.

Bilic, P., Christ, P.F., Vorontsov, E., Chlebus, G., Chen, H., Dou, Q., Fu, C.W., Han, X., Heng, P.A., Hesser, J., et al.: The liver tumor segmentation benchmark (lits). arXiv preprint arXiv:1901.04056 (2019)

Original

2019

Cited alongside, same era.

Khosravan, N., Mortazi, A., Wallace, M., Bagci, U.: PAN: Projective Adversarial Network for Medical Image Segmentation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (2019)

Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:2105.05537 (2021)

Original

2021

Later among the works it cites.

Huang, X., Deng, Z., Li, D., Yuan, X.: Missformer: An effective medical image segmentation transformer. arXiv preprint arXiv:2109.07162 (2021)

Original

2021

Later among the works it cites.

Ristea, N.C., Miron, A.I., Savencu, O., Georgescu, M.I., Verga, N., Khan, F.S., Ionescu, R.T.: Cytran: Cycle-consistent transformers for non-contrast to contrast ct translation. arXiv preprint arXiv:2110.06400 (2021)

Original

2021

Later among the works it cites.

Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., Bray, F.: Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 71

2021

Later among the works it cites.