Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets

Papandreou, G., Chen, L.C., Murphy, K.P., Yuille, A.L.: Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation. In: Proceedings of the IEEE international conference on computer vision. pp. 1742–1750 (2015)

2015

Earlier work this paper cites.

Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015)

2015

Earlier work this paper cites.

Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in neural information processing systems. pp. 1195–1204 (2017)

2017

Earlier work this paper cites.

Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2881–2890 (2017)

2017

Earlier work this paper cites.

Chen, H., Dou, Q., Yu, L., Qin, J., Heng, P.A.: Voxresnet: Deep voxelwise residual networks for brain segmentation from 3d mr images. NeuroImage 170

2018

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.

Gibson, E., Giganti, F., Hu, Y., Bonmati, E., Bandula, S., Gurusamy, K., Davidson, B., Pereira, S.P., Clarkson, M.J., Barratt, D.C.: Automatic multi-organ segmentation on abdominal ct with dense v-networks. IEEE transactions on medical imaging 37

2018

Cited alongside, same era.

Pan, X., Luo, P., Shi, J., Tang, X.: Two at once: Enhancing learning and generalization capacities via ibn-net. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 464–479 (2018)

2018

Cited alongside, same era.

Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., Agrawal, A.: Context encoding for semantic segmentation. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 7151–7160 (2018)

2018

Cited alongside, same era.

Zhang, Y., Xiang, T., Hospedales, T.M., Lu, H.: Deep mutual learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4320–4328 (2018)

2018

Cited alongside, same era.

Dmitriev, K., Kaufman, A.E.: Learning multi-class segmentations from single-class datasets. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 9501–9511 (2019)

2019

Later among the works it cites.

Heller, N., Sathianathen, N., Kalapara, A., Walczak, E., Moore, K., Kaluzniak, H., Rosenberg, J., Blake, P., Rengel, Z., Oestreich, M., et al.: The kits19 challenge data: 300 kidney tumor cases with clinical context, ct semantic segmentations, and surgical outcomes. arXiv preprint arXiv:1904.00445 (2019)

Original

2019

Later among the works it cites.

Simpson, A.L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., Van Ginneken, B., Kopp-Schneider, A., Landman, B.A., Litjens, G., Menze, B., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)

Original

2019

Later among the works it cites.

Zhou, Y., Li, Z., Bai, S., Wang, C., Chen, X., Han, M., Fishman, E., Yuille, A.L.: Prior-aware neural network for partially-supervised multi-organ segmentation. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 10672–10681 (2019)