Fetching the paper…
Reading the bibliography…
Automated segmentation of kidneys and kidney tumors is an important step in quantifying the tumor's morphometrical details to monitor the progression of the disease and accurately compare decisions regarding the kidney tumor treatment.
Sun, M., Abdollah, F., Bianchi, M., Trinh, Q.D., Jeldres, C., Thuret, R., Tian, Z., Shariat, S.F., Montorsi, F., Perrotte, P., et al.: Treatment management of small renal masses in the 21st century: a paradigm shift. Annals of surgical oncology 19
2012
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016)
2016
Earlier work this paper cites.
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: Fourth International Conference on 3D Vision (3DV) (2016)
2016
Earlier work this paper cites.
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 68
2018
Earlier work this paper cites.
Jackson, P., Hardcastle, N., Dawe, N., Kron, T., Hofman, M., Hicks, R.J.: Deep learning renal segmentation for fully automated radiation dose estimation in unsealed source therapy. Frontiers in oncology 8
2018
Earlier work this paper cites.
2018
Cited alongside, same era.
Thong, W., Kadoury, S., Piché, N., Pal, C.J.: Convolutional networks for kidney segmentation in contrast-enhanced ct scans. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6
2018
Cited alongside, same era.
Yang, G., Li, G., Pan, T., Kong, Y., Wu, J., Shu, H., Luo, L., Dillenseger, J.L., Coatrieux, J.L., Tang, L., et al.: Automatic segmentation of kidney and renal tumor in ct images based on 3d fully convolutional neural network with pyramid pooling module. In: 2018 24th International Conference on Pattern Recognition (ICPR). pp. 3790–3795. IEEE (2018)
2018
Cited alongside, same era.
2019
Closest in time.
2019
Closest in time.
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2019. CA: a cancer journal for clinicians 69
2019
Closest in time.
Xia, K.j., Yin, H.s., Zhang, Y.d.: Deep semantic segmentation of kidney and space-occupying lesion area based on scnn and resnet models combined with sift-flow algorithm. Journal of medical systems 43
2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Yin, K., Liu, C., Bardis, M., Martin, J., Liu, H., Ushinsky, A., Glavis-Bloom, J., Chantaduly, C., Chow, D.S., Houshyar, R., et al.: Deep learning segmentation of kidneys with renal cell carcinoma. (2019)
2019
Closest in time.