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In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis.
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A fast diffeomorphic image registration algorithm
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Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults
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Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain
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Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior
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Estimation of delivered dose in radiotherapy: the influence of registration uncertainty, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 548–555
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Longitudinal brain mri analysis with uncertain registration, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 647–654
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A locally adaptive regularization based on anisotropic diffusion for deformable image registration of sliding organs
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Bayesian characterization of uncertainty in intra-subject non-rigid registration
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Population of anatomically variable 4d xcat adult phantoms for imaging research and optimization
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On the importance of initialization and momentum in deep learning, in: International conference on machine learning, PMLR. pp. 1139–1147
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On rectified linear units for speech processing, in: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE. pp. 3517–3521
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U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image computing and computer-assisted intervention, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning, in: international conference on machine learning, PMLR. pp. 1050–1059
Incorporating ct prior information in the robust fuzzy c-means algorithm for qspect image segmentation, in: Medical Imaging 2019: Image Processing, International Society for Optics and Photonics. p. 109491W
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Deformable registration for dose accumulation, in: Seminars in radiation oncology, Elsevier. pp. 198–208
Chetty, I.J., Rosu-Bubulac, M., 2019 · 2019
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Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R., 2019 · 2019
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Resunet++: An advanced architecture for medical image segmentation, in: 2019 IEEE International Symposium on Multimedia (ISM), IEEE. pp. 225–2255
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Learning a probabilistic model for diffeomorphic registration
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Gal, Y., Ghahramani, Z., 2016 · 2016
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Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
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An empirical analysis of deep network loss surfaces
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On large-batch training for deep learning: Generalization gap and sharp minima
Keskar, N.S., Mudigere, D., Nocedal, J., Smelyanskiy, M., Tang, P.T.P., 2016 · 2016
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Understanding the effective receptive field in deep convolutional neural networks, in: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 4905–4913
Luo, W., Li, Y., Urtasun, R., Zemel, R., 2016 · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation, in: 2016 fourth international conference on 3D vision (3DV), IEEE. pp. 565–571
Milletari, F., Navab, N., Ahmadi, S.A., 2016 · 2016
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Krebs, J., Delingette, H., Mailhé, B., Ayache, N., Mansi, T., 2019 · 2019
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A deep learning framework for unsupervised affine and deformable image registration
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Generating anthropomorphic phantoms using fully unsupervised deformable image registration with convolutional neural networks
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How do vision transformers work?
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