Search Results for author: and Dinggang Shen

Found 2 papers, 1 papers with code

Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning Based Registration

no code implementations • 1. Sokooti, H., de Vos, B., Berendsen, F., Lelieveldt, B.P.F., Išgum, I., Staring, M.: Nonrigid image registration using multi-scale 3D convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 232–239. Springer, Cham (2017). https://doi.org/10.1007/978-3-319- 66182-7_27 2. Yang, X., et al.: Quicksilver fast predictive image registration–a deep learning approach. NeuroImage 158, 378–396 (2017) 3. Rohé, M.-M., Datar, M., Heimann, T., Sermesant, M., Pennec, X.: SVF-Net: learning deformable image registration using shape matching. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 266–274. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_31 4. Li, H., Fan, Y.: Non-Rigid Image Registration Using Self-Supervised Fully Convolutional Networks without Training Data. arXiv preprint arXiv:1801.04012 (2018) 5. Balakrishnan, G., 2018 • Jingfan Fan，Xiaohuan Cao， Zhong Xue, Pew-Thian Yap, and Dinggang Shen

The registration network is trained with feedback from the discrimination network, which is designed to judge whether a pair of registered images are sufficiently similar.

Image Registration

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Medical Image Synthesis with Deep Convolutional Adversarial Networks

1 code implementation • IEEE Transactions on Biomedical Engineering 2018 • Dong Nie, Roger Trullo, Jun Lian, Li Wang, Caroline Petitjean, Su Ruan, Qian Wang, and Dinggang Shen, Fellow, IEEE

To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN.

Image Generation

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