{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T11:44:12Z","timestamp":1783597452503,"version":"3.55.0"},"publisher-location":"Cham","reference-count":58,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030781903","type":"print"},{"value":"9783030781910","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-78191-0_1","type":"book-chapter","created":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T06:02:29Z","timestamp":1624168949000},"page":"3-17","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":99,"title":["HyperMorph: Amortized Hyperparameter Learning for Image Registration"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7583-5972","authenticated-orcid":false,"given":"Andrew","family":"Hoopes","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5511-0739","authenticated-orcid":false,"given":"Malte","family":"Hoffmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2413-1115","authenticated-orcid":false,"given":"Bruce","family":"Fischl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0992-0906","authenticated-orcid":false,"given":"John","family":"Guttag","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8422-0136","authenticated-orcid":false,"given":"Adrian V.","family":"Dalca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,6,14]]},"reference":[{"key":"1_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1007\/11866565_113","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2006","author":"V Arsigny","year":"2006","unstructured":"Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A log-Euclidean framework for statistics on diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006, Part I. LNCS, vol. 4190, pp. 924\u2013931. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11866565_113"},{"issue":"1","key":"1_CR2","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.neuroimage.2007.07.007","volume":"38","author":"J Ashburner","year":"2007","unstructured":"Ashburner, J.: A fast diffeomorphic image registration algorithm. Neuroimage 38(1), 95\u2013113 (2007)","journal-title":"Neuroimage"},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1006\/nimg.2000.0582","volume":"11","author":"J Ashburner","year":"2000","unstructured":"Ashburner, J., Friston, K.J.: Voxel-based morphometry-the methods. Neuroimage 11, 805\u2013821 (2000)","journal-title":"Neuroimage"},{"issue":"1","key":"1_CR4","first-page":"26","volume":"12","author":"BB Avants","year":"2008","unstructured":"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. MedIA 12(1), 26\u201341 (2008)","journal-title":"MedIA"},{"issue":"3","key":"1_CR5","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1016\/j.neuroimage.2010.09.025","volume":"54","author":"BB Avants","year":"2011","unstructured":"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(3), 2033\u20132044 (2011)","journal-title":"Neuroimage"},{"key":"1_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0734-189X(89)80014-3","volume":"46","author":"R Bajcsy","year":"1989","unstructured":"Bajcsy, R., Kovacic, S.: Multiresolution elastic matching. Comput. Vis. Graph. Image Process. 46, 1\u201321 (1989)","journal-title":"Comput. Vis. Graph. Image Process."},{"issue":"8","key":"1_CR7","first-page":"1788","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE TMI 38(8), 1788\u20131800 (2019)","journal-title":"IEEE TMI"},{"issue":"2","key":"1_CR8","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1023\/B:VISI.0000043755.93987.aa","volume":"61","author":"MF Beg","year":"2005","unstructured":"Beg, M.F., Miller, M.I., Trouv\u00e9, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. IJCV 61(2), 139\u2013157 (2005). https:\/\/doi.org\/10.1023\/B:VISI.0000043755.93987.aa","journal-title":"IJCV"},{"issue":"1","key":"1_CR9","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. JMLR 13(1), 281\u2013305 (2012)","journal-title":"JMLR"},{"key":"1_CR10","unstructured":"Bergstra, J.S., Bardenet, R., Bengio, Y., K\u00e9gl, B.: Algorithms for hyper-parameter optimization. In: NeurIPS, pp. 2546\u20132554 (2011)"},{"key":"1_CR11","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.neuroimage.2018.10.009","volume":"185","author":"SY Bookheimer","year":"2019","unstructured":"Bookheimer, S.Y., et al.: The lifespan human connectome project in aging: an overview. NeuroImage 185, 335\u2013348 (2019)","journal-title":"NeuroImage"},{"key":"1_CR12","unstructured":"Brock, A., Lim, T., Ritchie, J.M., Weston, N.: Smash: one-shot model architecture search through hypernetworks. arXiv preprint arXiv:1708.05344 (2017)"},{"issue":"9","key":"1_CR13","first-page":"1216","volume":"24","author":"Y Cao","year":"2005","unstructured":"Cao, Y., Miller, M.I., Winslow, R.L., Younes, L.: Large deformation diffeomorphic metric mapping of vector fields. IEEE TMI 24(9), 1216\u20131230 (2005)","journal-title":"IEEE TMI"},{"key":"1_CR14","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.neuroimage.2015.03.069","volume":"144","author":"A Dagley","year":"2017","unstructured":"Dagley, A., et al.: Harvard aging brain study: dataset and accessibility. NeuroImage 144, 255\u2013258 (2017)","journal-title":"NeuroImage"},{"key":"1_CR15","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.media.2019.07.006","volume":"57","author":"AV Dalca","year":"2019","unstructured":"Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.: Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med. Image Anal. 57, 226\u2013236 (2019)","journal-title":"Med. Image Anal."},{"key":"1_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/978-3-319-47118-1_8","volume-title":"Patch-Based Techniques in Medical Imaging","author":"AV Dalca","year":"2016","unstructured":"Dalca, A.V., Bobu, A., Rost, N.S., Golland, P.: Patch-based discrete registration of clinical brain images. In: Wu, G., Coup\u00e9, P., Zhan, Y., Munsell, B.C., Rueckert, D. (eds.) Patch-MI 2016. LNCS, vol. 9993, pp. 60\u201367. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-47118-1_8"},{"issue":"6","key":"1_CR17","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/mp.2013.78","volume":"19","author":"A DI Martino","year":"2014","unstructured":"DI Martino, A., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659\u2013667 (2014)","journal-title":"Mol. Psychiatry"},{"issue":"3","key":"1_CR18","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297\u2013302 (1945)","journal-title":"Ecology"},{"key":"1_CR19","unstructured":"Domhan, T., Springenberg, J.T., Hutter, F.: Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)"},{"issue":"2","key":"1_CR20","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl, B.: Freesurfer. Neuroimage 62(2), 774\u2013781 (2012)","journal-title":"Neuroimage"},{"key":"1_CR21","unstructured":"Franceschi, L., Frasconi, P., Salzo, S., Grazzi, R., Pontil, M.: Bilevel programming for hyperparameter optimization and meta-learning. arXiv preprint arXiv:1806.04910 (2018)"},{"issue":"6","key":"1_CR22","first-page":"731","volume":"12","author":"B Glocker","year":"2008","unstructured":"Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through MRFs and efficient linear programming. MedIA 12(6), 731\u2013741 (2008)","journal-title":"MedIA"},{"key":"1_CR23","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS, pp. 249\u2013256 (2010)"},{"issue":"3","key":"1_CR24","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s12021-013-9184-3","volume":"11","author":"RL Gollub","year":"2013","unstructured":"Gollub, R.L., et al.: The MCIC collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia. Neuroinformatics 11(3), 367\u2013388 (2013). https:\/\/doi.org\/10.1007\/s12021-013-9184-3","journal-title":"Neuroinformatics"},{"key":"1_CR25","unstructured":"Ha, D., Dai, A., Le, Q.V.: Hypernetworks. arXiv preprint arXiv:1609.09106 (2016)"},{"key":"1_CR26","unstructured":"Hoffmann, M., Billot, B., Iglesias, J.E., Fischl, B., Dalca, A.V.: Learning image registration without images (2020)"},{"key":"1_CR27","first-page":"1","volume":"49","author":"Y Hu","year":"2018","unstructured":"Hu, Y., et al.: Weakly-supervised convolutional neural networks for multimodal image registration. MedIA 49, 1\u201313 (2018)","journal-title":"MedIA"},{"key":"1_CR28","unstructured":"Jamieson, K., Talwalkar, A.: Non-stochastic best arm identification and hyperparameter optimization. In: AISTATS, pp. 240\u2013248 (2016)"},{"issue":"8","key":"1_CR29","first-page":"1357","volume":"9","author":"SC Joshi","year":"2000","unstructured":"Joshi, S.C., Miller, M.I.: Landmark matching via large deformation diffeomorphisms. IEEE TIP 9(8), 1357\u20131370 (2000)","journal-title":"IEEE TIP"},{"key":"1_CR30","unstructured":"Kandasamy, K., Dasarathy, G., Schneider, J., P\u00f3czos, B.: Multi-fidelity bayesian optimisation with continuous approximations. arXiv preprint arXiv:1703.06240 (2017)"},{"key":"1_CR31","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"1_CR32","unstructured":"Klein, A., Falkner, S., Springenberg, J.T., Hutter, F.: Learning curve prediction with Bayesian neural networks (2016)"},{"key":"1_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1007\/978-3-030-30493-5_48","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Workshop and Special Sessions","author":"S Klocek","year":"2019","unstructured":"Klocek, S., Maziarka, \u0141., Wo\u0142czyk, M., Tabor, J., Nowak, J., \u015amieja, M.: Hypernetwork functional image representation. In: Tetko, I.V., K\u016frkov\u00e1, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11731, pp. 496\u2013510. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30493-5_48"},{"issue":"9","key":"1_CR34","first-page":"2165","volume":"38","author":"J Krebs","year":"2019","unstructured":"Krebs, J., Delingette, H., Mailh\u00e9, B., Ayache, N., Mansi, T.: Learning a probabilistic model for diffeomorphic registration. IEEE TMI 38(9), 2165\u20132176 (2019)","journal-title":"IEEE TMI"},{"key":"1_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1007\/978-3-319-66182-7_40","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"J Krebs","year":"2017","unstructured":"Krebs, J., et al.: Robust non-rigid registration through agent-based action learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 344\u2013352. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_40"},{"issue":"1","key":"1_CR36","first-page":"6765","volume":"18","author":"L Li","year":"2017","unstructured":"Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., Talwalkar, A.: Hyperband: a novel bandit-based approach to hyperparameter optimization. JMLR 18(1), 6765\u20136816 (2017)","journal-title":"JMLR"},{"key":"1_CR37","unstructured":"Lorraine, J., Duvenaud, D.: Stochastic hyperparameter optimization through hypernetworks. arXiv preprint arXiv:1802.09419 (2018)"},{"key":"1_CR38","unstructured":"Luketina, J., Berglund, M., Greff, K., Raiko, T.: Scalable gradient-based tuning of continuous regularization hyperparameters. In: ICML, pp. 2952\u20132960 (2016)"},{"key":"1_CR39","unstructured":"MacKay, M., Vicol, P., Lorraine, J., Duvenaud, D., Grosse, R.: Self-tuning networks: Bilevel optimization of hyperparameters using structured best-response functions. arXiv preprint arXiv:1903.03088 (2019)"},{"key":"1_CR40","unstructured":"Maclaurin, D., Duvenaud, D., Adams, R.: Gradient-based hyperparameter optimization through reversible learning. In: ICML, pp. 2113\u20132122 (2015)"},{"issue":"9","key":"1_CR41","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1162\/jocn.2007.19.9.1498","volume":"19","author":"DS Marcus","year":"2007","unstructured":"Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19(9), 1498\u20131507 (2007)","journal-title":"J. Cogn. Neurosci."},{"issue":"4","key":"1_CR42","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/j.pneurobio.2011.09.005","volume":"95","author":"K Marek","year":"2011","unstructured":"Marek, K., et al.: The Parkinson progression marker initiative (PPMI). Prog. Neurobi. 95(4), 629\u2013635 (2011)","journal-title":"Prog. Neurobi."},{"key":"1_CR43","first-page":"62","volume":"6","author":"MP Milham","year":"2012","unstructured":"Milham, M.P., Fair, D., Mennes, M., Mostofsky, S.H., et al.: The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Frontiers Syst. Neurosci. 6, 62 (2012)","journal-title":"Frontiers Syst. Neurosci."},{"issue":"27","key":"1_CR44","doi-asserted-by":"publisher","first-page":"9685","DOI":"10.1073\/pnas.0503892102","volume":"102","author":"MI Miller","year":"2005","unstructured":"Miller, M.I., Beg, M.F., Ceritoglu, C., Stark, C.: Increasing the power of functional maps of the medial temporal lobe by using large deformation diffeomorphic metric mapping. PNAS 102(27), 9685\u20139690 (2005)","journal-title":"PNAS"},{"key":"1_CR45","unstructured":"Pedregosa, F.: Hyperparameter optimization with approximate gradient. arXiv preprint arXiv:1602.02355 (2016)"},{"key":"1_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1007\/978-3-319-66182-7_31","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"M-M Roh\u00e9","year":"2017","unstructured":"Roh\u00e9, 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, Part I. LNCS, vol. 10433, pp. 266\u2013274. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_31"},{"key":"1_CR47","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"8","key":"1_CR48","first-page":"712","volume":"18","author":"D Rueckert","year":"1999","unstructured":"Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformation: Application to breast mr images. IEEE TMI 18(8), 712\u2013721 (1999)","journal-title":"IEEE TMI"},{"key":"1_CR49","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1007\/978-1-4471-2063-6_107","volume-title":"ICANN 1993","author":"J Schmidhuber","year":"1993","unstructured":"Schmidhuber, J.: A \u2018self-referential\u2019 weight matrix. In: Gielen, S., Kappen, B. (eds.) ICANN 1993, pp. 446\u2013450. Springer, London (1993). https:\/\/doi.org\/10.1007\/978-1-4471-2063-6_107"},{"key":"1_CR50","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1007\/978-3-319-66182-7_27","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"H Sokooti","year":"2017","unstructured":"Sokooti, H., de Vos, B., Berendsen, F., Lelieveldt, B.P.F., I\u0161gum, 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, Part I. LNCS, vol. 10433, pp. 232\u2013239. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_27"},{"issue":"3","key":"1_CR51","doi-asserted-by":"publisher","first-page":"e1001779","DOI":"10.1371\/journal.pmed.1001779","volume":"12","author":"C Sudlow","year":"2015","unstructured":"Sudlow, C., et al.: UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. Plos med 12(3), e1001779 (2015)","journal-title":"Plos med"},{"issue":"1","key":"1_CR52","doi-asserted-by":"publisher","first-page":"S61","DOI":"10.1016\/j.neuroimage.2008.10.040","volume":"45","author":"T Vercauteren","year":"2009","unstructured":"Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(1), S61\u2013S72 (2009)","journal-title":"NeuroImage"},{"issue":"2","key":"1_CR53","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1023\/A:1007958904918","volume":"24","author":"P Viola","year":"1997","unstructured":"Viola, P., Wells III, W.M.: Alignment by maximization of mutual information. Int. J. Comput. Vis. 24(2), 137\u2013154 (1997). https:\/\/doi.org\/10.1023\/A:1007958904918","journal-title":"Int. J. Comput. Vis."},{"key":"1_CR54","first-page":"128","volume":"52","author":"BD de Vos","year":"2019","unstructured":"de Vos, B.D., Berendsen, F.F., Viergever, M.A., Sokooti, H., Staring, M., I\u0161gum, I.: A deep learning framework for unsupervised affine and deformable image registration. MedIA 52, 128\u2013143 (2019)","journal-title":"MedIA"},{"key":"1_CR55","unstructured":"Weiner, M.W.: Alzheimer\u2019s disease neuroimaging initiative (ADNI) database (2003)"},{"issue":"7","key":"1_CR56","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1109\/TBME.2015.2496253","volume":"63","author":"G Wu","year":"2015","unstructured":"Wu, G., Kim, M., Wang, Q., Munsell, B.C., Shen, D.: Scalable high-performance image registration framework by unsupervised deep feature representations learning. IEEE Trans. Biomed. Eng. 63(7), 1505\u20131516 (2015)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"1_CR57","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.neuroimage.2017.07.008","volume":"158","author":"X Yang","year":"2017","unstructured":"Yang, X., Kwitt, R., Styner, M., Niethammer, M.: Quicksilver: fast predictive image registration - a deep learning approach. NeuroImage 158, 378\u2013396 (2017)","journal-title":"NeuroImage"},{"key":"1_CR58","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1007\/978-3-319-59050-9_44","volume-title":"Information Processing in Medical Imaging","author":"M Zhan","year":"2017","unstructured":"Zhan, M., et al.: Frequency diffeomorphisms for efficient image registration. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 559\u2013570. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_44"}],"container-title":["Lecture Notes in Computer Science","Information Processing in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78191-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T06:02:44Z","timestamp":1624168964000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78191-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030781903","9783030781910"],"references-count":58,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78191-0_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"14 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmi2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ipmi2021.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"200","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}