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Syst."},{"key":"10.1016\/j.media.2022.102615_b89","series-title":"Calibrating uncertainties in object localization task","author":"Phan","year":"2018"},{"key":"10.1016\/j.media.2022.102615_b90","series-title":"Medical Imaging with Deep Learning","article-title":"Learning diffeomorphic and modality-invariant registration using B-splines","author":"Qiu","year":"2021"},{"key":"10.1016\/j.media.2022.102615_b91","series-title":"Do vision transformers see like convolutional neural networks?","author":"Raghu","year":"2021"},{"key":"10.1016\/j.media.2022.102615_b92","doi-asserted-by":"crossref","unstructured":"Redmon,\u00a0J., Divvala,\u00a0S., Girshick,\u00a0R., Farhadi,\u00a0A., 2016. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"10.1016\/j.media.2022.102615_b93","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"548","article-title":"Estimation of delivered dose in radiotherapy: the influence of registration uncertainty","author":"Risholm","year":"2011"},{"issue":"5","key":"10.1016\/j.media.2022.102615_b94","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.media.2013.03.002","article-title":"Bayesian characterization of uncertainty in intra-subject non-rigid registration","volume":"17","author":"Risholm","year":"2013","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2022.102615_b95","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"266","article-title":"SVF-net: Learning deformable image registration using shape matching","author":"Roh\u00e9","year":"2017"},{"key":"10.1016\/j.media.2022.102615_b96","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"issue":"8","key":"10.1016\/j.media.2022.102615_b97","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/42.796284","article-title":"Nonrigid registration using free-form deformations: application to breast MR images","volume":"18","author":"Rueckert","year":"1999","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2022.102615_b98","article-title":"How does batch normalization help optimization?","volume":"31","author":"Santurkar","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"4","key":"10.1016\/j.media.2022.102615_b99","doi-asserted-by":"crossref","DOI":"10.1118\/1.4794178","article-title":"Population of anatomically variable 4D XCAT adult phantoms for imaging research and optimization","volume":"40","author":"Segars","year":"2013","journal-title":"Med. Phys."},{"issue":"9","key":"10.1016\/j.media.2022.102615_b100","doi-asserted-by":"crossref","first-page":"4902","DOI":"10.1118\/1.3480985","article-title":"4D XCAT phantom for multimodality imaging research","volume":"37","author":"Segars","year":"2010","journal-title":"Med. Phys."},{"key":"10.1016\/j.media.2022.102615_b101","series-title":"Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021","author":"Siebert","year":"2021"},{"key":"10.1016\/j.media.2022.102615_b102","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"647","article-title":"Longitudinal brain MRI analysis with uncertain registration","author":"Simpson","year":"2011"},{"key":"10.1016\/j.media.2022.102615_b103","series-title":"Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, vol. 11006","article-title":"Super-convergence: Very fast training of neural networks using large learning rates","author":"Smith","year":"2019"},{"key":"10.1016\/j.media.2022.102615_b104","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"232","article-title":"Nonrigid image registration using multi-scale 3D convolutional neural networks","author":"Sokooti","year":"2017"},{"key":"10.1016\/j.media.2022.102615_b105","series-title":"International Conference on Machine Learning","first-page":"1139","article-title":"On the importance of initialization and momentum in deep learning","author":"Sutskever","year":"2013"},{"key":"10.1016\/j.media.2022.102615_b106","first-page":"698","article-title":"A mean-field variational inference approach to deep image prior for inverse problems in medical imaging","author":"T\u00f6lle","year":"2021","journal-title":"Med. Imag. Deep Learn."},{"key":"10.1016\/j.media.2022.102615_b107","series-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"issue":"1","key":"10.1016\/j.media.2022.102615_b108","doi-asserted-by":"crossref","first-page":"S61","DOI":"10.1016\/j.neuroimage.2008.10.040","article-title":"Diffeomorphic demons: Efficient non-parametric image registration","volume":"45","author":"Vercauteren","year":"2009","journal-title":"NeuroImage"},{"issue":"17","key":"10.1016\/j.media.2022.102615_b109","doi-asserted-by":"crossref","first-page":"N391","DOI":"10.1088\/1361-6560\/aa8133","article-title":"Representing the dosimetric impact of deformable image registration errors","volume":"62","author":"Vickress","year":"2017","journal-title":"Phys. Med. Biol."},{"issue":"2","key":"10.1016\/j.media.2022.102615_b110","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/A:1007958904918","article-title":"Alignment by maximization of mutual information","volume":"24","author":"Viola","year":"1997","journal-title":"Int. J. Comput. Vis."},{"issue":"2","key":"10.1016\/j.media.2022.102615_b111","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1109\/TMI.2016.2610583","article-title":"Isotropic total variation regularization of displacements in parametric image registration","volume":"36","author":"Vishnevskiy","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.media.2022.102615_b112","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.media.2022.102615_b113","series-title":"Transbts: Multimodal brain tumor segmentation using transformer","author":"Wang","year":"2021"},{"key":"10.1016\/j.media.2022.102615_b114","series-title":"TED-net: Convolution-free T2T vision transformer-based encoder-decoder dilation network for low-dose CT denoising","author":"Wang","year":"2021"},{"key":"10.1016\/j.media.2022.102615_b115","series-title":"Pyramid vision transformer: A versatile backbone for dense prediction without convolutions","author":"Wang","year":"2021"},{"key":"10.1016\/j.media.2022.102615_b116","series-title":"Proceedings 2000 International Conference on Image Processing (Cat. No. 00CH37101), vol. 1","first-page":"493","article-title":"Robust image registration using log-polar transform","author":"Wolberg","year":"2000"},{"key":"10.1016\/j.media.2022.102615_b117","series-title":"CoTr: Efficiently bridging CNN and transformer for 3D medical image segmentation","author":"Xie","year":"2021"},{"key":"10.1016\/j.media.2022.102615_b118","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","article-title":"Double-uncertainty guided spatial and temporal consistency regularization weighting for learning-based abdominal registration","author":"Xu","year":"2022"},{"key":"10.1016\/j.media.2022.102615_b119","series-title":"Deep Learning and Data Labeling for Medical Applications","first-page":"48","article-title":"Fast predictive image registration","author":"Yang","year":"2016"},{"key":"10.1016\/j.media.2022.102615_b120","series-title":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","first-page":"858","article-title":"Fast predictive multimodal image registration","author":"Yang","year":"2017"},{"key":"10.1016\/j.media.2022.102615_b121","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.neuroimage.2017.07.008","article-title":"Quicksilver: Fast predictive image registration\u2013a deep learning approach","volume":"158","author":"Yang","year":"2017","journal-title":"NeuroImage"},{"key":"10.1016\/j.media.2022.102615_b122","series-title":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"3517","article-title":"On rectified linear units for speech processing","author":"Zeiler","year":"2013"},{"key":"10.1016\/j.media.2022.102615_b123","doi-asserted-by":"crossref","unstructured":"Zhai,\u00a0X., Kolesnikov,\u00a0A., Houlsby,\u00a0N., Beyer,\u00a0L., 2022. Scaling vision transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 12104\u201312113.","DOI":"10.1109\/CVPR52688.2022.01179"},{"key":"10.1016\/j.media.2022.102615_b124","series-title":"Inverse-consistent deep networks for unsupervised deformable image registration","author":"Zhang","year":"2018"},{"issue":"6","key":"10.1016\/j.media.2022.102615_b125","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1002\/mp.12259","article-title":"A new CT reconstruction technique using adaptive deformation recovery and intensity correction (ADRIC)","volume":"44","author":"Zhang","year":"2017","journal-title":"Med. Phys."},{"key":"10.1016\/j.media.2022.102615_b126","series-title":"Transct: Dual-path transformer for low dose computed tomography","author":"Zhang","year":"2021"},{"key":"10.1016\/j.media.2022.102615_b127","series-title":"Nnformer: Interleaved transformer for volumetric segmentation","author":"Zhou","year":"2021"},{"issue":"6","key":"10.1016\/j.media.2022.102615_b128","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med. 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