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Deformable image registration is one of the fundamental tasks in medical imaging.
Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. International journal of computer vision 61
2005
Earlier work this paper cites.
Ashburner, J.: A fast diffeomorphic image registration algorithm. Neuroimage 38
2007
Earlier work this paper cites.
Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical image analysis 12
2008
Earlier work this paper cites.
Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE transactions on medical imaging 29
2009
Earlier work this paper cites.
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage 45
2009
Earlier work this paper cites.
Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D.H., Hawk, S.T., Van Knippenberg, A.: Presentation and validation of the radboud faces database. Cognition and emotion 24
2010
Earlier work this paper cites.
Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ants similarity metric performance in brain image registration. Neuroimage 54
2011
Earlier work this paper cites.
Fischl, B.: Freesurfer. Neuroimage 62
2012
Earlier work this paper cites.
Onofrey, J.A., Staib, L.H., Papademetris, X.: Semi-supervised learning of nonrigid deformations for image registration. In: International MICCAI Workshop on Medical Computer Vision. pp. 13–23. Springer (2013)
2013
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision. pp. 1026–1034 (2015)
2015
Earlier work this paper cites.
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. Advances in neural information processing systems 28
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (Poster) (2015)
2015
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning. pp. 2256–2265. PMLR (2015)
2015
Earlier work this paper cites.
Cao, X., Yang, J., Zhang, J., Nie, D., Kim, M., Wang, Q., Shen, D.: Deformable image registration based on similarity-steered cnn regression. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 300–308. Springer (2017)
2017
Earlier work this paper cites.
Rohé, M.M., Datar, M., Heimann, T., Sermesant, M., Pennec, X.: Svf-net: Learning deformable image registration using shape matching. In: International conference on medical image computing and computer-assisted intervention. pp. 266–274. Springer (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. In: Advances in neural information processing systems. pp. 5998–6008 (2017)
2017
Earlier work this paper cites.
Yang, X., Kwitt, R., Styner, M., Niethammer, M.: Quicksilver: Fast predictive image registration–a deep learning approach. NeuroImage 158
2017
Cited alongside, same era.
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: An unsupervised learning model for deformable medical image registration. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 9252–9260 (2018)
2018
Cited alongside, same era.
Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.A., Cetin, I., Lekadir, K., Camara, O., Ballester, M.A.G., et al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging 37
2018
Cited alongside, same era.
Cao, X., Yang, J., Wang, L., Xue, Z., Wang, Q., Shen, D.: Deep learning based inter-modality image registration supervised by intra-modality similarity. In: International workshop on machine learning in medical imaging. pp. 55–63. Springer (2018)
Fadnavis, S., Batson, J., Garyfallidis, E.: Patch2self: Denoising diffusion mri with self-supervised learning. Advances in Neural Information Processing Systems 33
2020
Later among the works it cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33
2020
Later among the works it cites.
Lei, Y., Fu, Y., Wang, T., Liu, Y., Patel, P., Curran, W.J., Liu, T., Yang, X.: 4d-ct deformable image registration using multiscale unsupervised deep learning. Physics in Medicine & Biology 65
2020
Later among the works it cites.
Mok, T.C., Chung, A.: Fast symmetric diffeomorphic image registration with convolutional neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4644–4653 (2020)
2020
Later among the works it cites.
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2018
Cited alongside, same era.
Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning for fast probabilistic diffeomorphic registration. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 729–738. Springer (2018)
2018
Cited alongside, same era.
Elfwing, S., Uchibe, E., Doya, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks 107
2018
Cited alongside, same era.
Hu, Y., Modat, M., Gibson, E., Li, W., Ghavami, N., Bonmati, E., Wang, G., Bandula, S., Moore, C.M., Emberton, M., et al.: Weakly-supervised convolutional neural networks for multimodal image registration. Medical image analysis 49
2018
Cited alongside, same era.
Krebs, J., Mansi, T., Mailhé, B., Ayache, N., Delingette, H.: Unsupervised probabilistic deformation modeling for robust diffeomorphic registration. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 101–109. Springer (2018)
2018
Cited alongside, same era.
Mahapatra, D., Antony, B., Sedai, S., Garnavi, R.: Deformable medical image registration using generative adversarial networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). pp. 1449–1453. IEEE (2018)
2018
Cited alongside, same era.
Wu, Y., He, K.: Group normalization. In: Proceedings of the European conference on computer vision (ECCV). pp. 3–19 (2018)
2018
Cited alongside, same era.
Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Medical image analysis 57
2019
Cited alongside, same era.
LaMontagne, P.J., Benzinger, T.L., Morris, J.C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., Vlassenko, A., et al.: Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease. MedRxiv (2019)
2019
Cited alongside, same era.
2020
Later among the works it cites.
Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. In: International Conference on Learning Representations (2020)
2020
Later among the works it cites.
Choi, J., Kim, S., Jeong, Y., Gwon, Y., Yoon, S.: Ilvr: Conditioning method for denoising diffusion probabilistic models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 14367–14376 (2021)
2021
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2021
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Kim, B., Kim, D.H., Park, S.H., Kim, J., Lee, J.G., Ye, J.C.: Cyclemorph: Cycle consistent unsupervised deformable image registration. Medical Image Analysis 71
2021
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Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning. pp. 8162–8171. PMLR (2021)
2021
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2021
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2021
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Vahdat, A., Kreis, K., Kautz, J.: Score-based generative modeling in latent space. Advances in Neural Information Processing Systems 34
2021
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Ho, J., Saharia, C., Chan, W., Fleet, D.J., Norouzi, M., Salimans, T.: Cascaded diffusion models for high fidelity image generation. Journal of Machine Learning Research 23
2022
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