{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T12:12:07Z","timestamp":1770293527975,"version":"3.49.0"},"reference-count":133,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>Multi-modality imaging improves disease diagnosis and reveals distinct deviations in tissues with anatomical properties. The existence of completely aligned and paired multi-modality neuroimaging data has proved its effectiveness in brain research. However, collecting fully aligned and paired data is expensive or even impractical, since it faces many difficulties, including high cost, long acquisition time, image corruption, and privacy issues. An alternative solution is to explore unsupervised or weakly supervised learning methods to synthesize the absent neuroimaging data. In this article, we provide a comprehensive review of cross-modality synthesis for neuroimages, from the perspectives of weakly supervised and unsupervised settings, loss functions, evaluation metrics, imaging modalities, datasets, and downstream applications based on synthesis. We begin by highlighting several opening challenges for cross-modality neuroimage synthesis. Then, we discuss representative architectures of cross-modality synthesis methods under different supervisions. This is followed by a stepwise in-depth analysis to evaluate how cross-modality neuroimage synthesis improves the performance of its downstream tasks. Finally, we summarize the existing research findings and point out future research directions. All resources are available at https:\/\/github.com\/M-3LAB\/awesome-multimodal-brain-image-systhesis.<\/jats:p>","DOI":"10.1145\/3625227","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T12:41:55Z","timestamp":1695386515000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Cross-modality Neuroimage Synthesis: A Survey"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8433-8153","authenticated-orcid":false,"given":"Guoyang","family":"Xie","sequence":"first","affiliation":[{"name":"Southern University of Science and Technology, China and University of Surrey, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9569-269X","authenticated-orcid":false,"given":"Yawen","family":"Huang","sequence":"additional","affiliation":[{"name":"Jarvis Research Center, Tencent YouTu Lab, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5916-8965","authenticated-orcid":false,"given":"Jinbao","family":"Wang","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9202-0947","authenticated-orcid":false,"given":"Jiayi","family":"Lyu","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1701-9141","authenticated-orcid":false,"given":"Feng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2195-2847","authenticated-orcid":false,"given":"Yefeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Jarvis Research Center, Tencent YouTu Lab, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1100-0631","authenticated-orcid":false,"given":"Yaochu","family":"Jin","sequence":"additional","affiliation":[{"name":"Westlake University, China and University of Surrey, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"issue":"11","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","article-title":"K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation","volume":"54","author":"Aharon Michal","year":"2006","unstructured":"Michal Aharon, Michael Elad, and Alfred Bruckstein. 2006. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54, 11 (2006), 4311\u20134322.","journal-title":"IEEE Transactions on Signal Processing"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","first-page":"108109","DOI":"10.1016\/j.dib.2022.108109","article-title":"Unpaired MR-CT brain dataset for unsupervised image translation","volume":"42","author":"Al-Kadi Omar S.","year":"2022","unstructured":"Omar S. Al-Kadi, Israa Almallahi, Alaa Abu-Srhan, A. M. Mohammad Abushariah, and Waleed Mahafza. 2022. Unpaired MR-CT brain dataset for unsupervised image translation. Data in Brief 42 (2022), 108109.","journal-title":"Data in Brief"},{"key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"2072","DOI":"10.1109\/TMI.2011.2162529","article-title":"A combined manifold learning analysis of shape and appearance to characterize neonatal brain development","volume":"30","author":"Aljabar Paul","year":"2011","unstructured":"Paul Aljabar, Robin Wolz, Latha Srinivasan, Serena J. Counsell, Mary A. Rutherford, Anthony David Edwards, Joseph V. Hajnal, and Daniel Rueckert. 2011. A combined manifold learning analysis of shape and appearance to characterize neonatal brain development. IEEE Transactions on Medical Imaging 30 (2011), 2072\u20132086.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"3","key":"e_1_3_1_5_2","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1016\/j.neuroimage.2010.09.025","article-title":"A reproducible evaluation of ANTs similarity metric performance in brain image registration","volume":"54","author":"Avants Brian B.","year":"2011","unstructured":"Brian B. Avants, Nicholas J. Tustison, Gang Song, Philip A. Cook, Arno Klein, and James C. Gee. 2011. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54, 3 (2011), 2033\u20132044.","journal-title":"Neuroimage"},{"key":"e_1_3_1_6_2","article-title":"Can we gain more from orthogonality regularizations in training deep networks?","volume":"31","author":"Bansal Nitin","year":"2018","unstructured":"Nitin Bansal, Xiaohan Chen, and Zhangyang Wang. 2018. Can we gain more from orthogonality regularizations in training deep networks? Advances in Neural Information Processing Systems 31 (2018).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_7_2","doi-asserted-by":"crossref","first-page":"106531","DOI":"10.1016\/j.cmpb.2021.106531","article-title":"DDA-Net: Unsupervised cross-modality medical image segmentation via dual domain adaptation","volume":"213","author":"Bian Xuesheng","year":"2022","unstructured":"Xuesheng Bian, Xiongbiao Luo, Cheng Wang, Weiquan Liu, and Xiuhong Lin. 2022. DDA-Net: Unsupervised cross-modality medical image segmentation via dual domain adaptation. Computer Methods and Programs in Biomedicine 213 (2022), 106531.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"e_1_3_1_8_2","first-page":"1159","article-title":"Contrast-enhanced brain MRI synthesis with deep learning: Key input modalities and asymptotic performance","author":"B\u00f4ne Alexandre","year":"2021","unstructured":"Alexandre B\u00f4ne, Samy Ammari, Jean-Philippe Lamarque, Mickael Elhaik, \u00c9milie Chouzenoux, Fran\u00e7ois Nicolas, Philippe Robert, Corinne Balleyguier, Nathalie Lassau, and Marc-Michel Roh\u00e9. 2021. Contrast-enhanced brain MRI synthesis with deep learning: Key input modalities and asymptotic performance. In IEEE 18th International Symposium on Biomedical Imaging, 1159\u20131163.","journal-title":"IEEE 18th International Symposium on Biomedical Imaging"},{"issue":"3","key":"e_1_3_1_9_2","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1109\/TMI.2017.2764326","article-title":"Multimodal MR synthesis via modality-invariant latent representation","volume":"37","author":"Chartsias Agisilaos","year":"2017","unstructured":"Agisilaos Chartsias, Thomas Joyce, Mario Valerio Giuffrida, and Sotirios A. Tsaftaris. 2017. Multimodal MR synthesis via modality-invariant latent representation. IEEE Transactions on Medical Imaging 37, 3 (2017), 803\u2013814.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1109\/TMI.2017.2764326","article-title":"Multimodal MR synthesis via modality-invariant latent representation","volume":"37","author":"Chartsias Agisilaos","year":"2018","unstructured":"Agisilaos Chartsias, Thomas Joyce, Mario Valerio Giuffrida, and Sotirios A. Tsaftaris. 2018. Multimodal MR synthesis via modality-invariant latent representation. IEEE Transactions on Medical Imaging 37 (2018), 803\u2013814.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_11_2","article-title":"Semantic-aware generative adversarial nets for unsupervised domain adaptation in Chest X-ray segmentation","author":"Chen Cheng","year":"2018","unstructured":"Cheng Chen, Qi Dou, Hao Chen, and Pheng-Ann Heng. 2018. Semantic-aware generative adversarial nets for unsupervised domain adaptation in Chest X-ray segmentation. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_12_2","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.1109\/TMI.2020.2972701","article-title":"Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation","volume":"39","author":"Chen Cheng","year":"2020","unstructured":"Cheng Chen, Qi Dou, Hao Chen, Jing Qin, and Pheng-Ann Heng. 2020. Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation. IEEE Transactions on Medical Imaging 39 (2020), 2494\u20132505.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","first-page":"102133","DOI":"10.1016\/j.media.2021.102133","article-title":"ABCnet: Adversarial bias correction network for infant brain MR images","volume":"72","author":"Chen Liangjun","year":"2021","unstructured":"Liangjun Chen, Zhengwang Wu, Dan Hu, Fan Wang, J. Keith Smith, Weili Lin, Li Wang, Dinggang Shen, and Gang Li. 2021. ABCnet: Adversarial bias correction network for infant brain MR images. Medical Image Analysis 72 (2021), 102133.","journal-title":"Medical Image Analysis"},{"key":"e_1_3_1_14_2","first-page":"8168","article-title":"Reusing discriminators for encoding: Towards unsupervised image-to-image translation","author":"Chen Runfa","year":"2020","unstructured":"Runfa Chen, Wenbing Huang, Binghui Huang, Fuchun Sun, and Bin Fang. 2020. Reusing discriminators for encoding: Towards unsupervised image-to-image translation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 8168\u20138177.","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_1_15_2","article-title":"Unsupervised multi-modal medical image registration via discriminator-free image-to-image translation","author":"Chen Zekang","year":"2022","unstructured":"Zekang Chen, Jia Wei, and Rui Li. 2022. Unsupervised multi-modal medical image registration via discriminator-free image-to-image translation. In International Joint Conferences on Artificial Intelligence.","journal-title":"International Joint Conferences on Artificial Intelligence"},{"issue":"6","key":"e_1_3_1_16_2","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1016\/j.sigpro.2012.09.011","article-title":"Sparse representation and learning in visual recognition: Theory and applications","volume":"93","author":"Cheng Hong","year":"2013","unstructured":"Hong Cheng, Zicheng Liu, Lu Yang, and Xuewen Chen. 2013. Sparse representation and learning in visual recognition: Theory and applications. Signal Processing 93, 6 (2013), 1408\u20131425.","journal-title":"Signal Processing"},{"key":"e_1_3_1_17_2","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TMI.2019.2901750","article-title":"Image synthesis in multi-contrast MRI with conditional generative adversarial networks","volume":"38","author":"Dar Salman Ul Hassan","year":"2019","unstructured":"Salman Ul Hassan Dar, Mahmut Yurt, Levent Karacan, Aykut Erdem, Erkut Erdem, and Tolga \u00c7ukur. 2019. Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Transactions on Medical Imaging 38 (2019), 2375\u20132388.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"12","key":"e_1_3_1_18_2","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1109\/LSP.2012.2224655","article-title":"Sparse and redundant representation modeling\u2019What next?","volume":"19","author":"Elad Michael","year":"2012","unstructured":"Michael Elad. 2012. Sparse and redundant representation modeling\u2019What next? IEEE Signal Processing Letters 19, 12 (2012), 922\u2013928.","journal-title":"IEEE Signal Processing Letters"},{"issue":"4","key":"e_1_3_1_19_2","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1017\/S1041610209009405","article-title":"The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: Methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer\u2019s disease","volume":"21","year":"2009","unstructured":"Kathryn A. Ellis, Ashley I. Bush, David Darby, Daniela De Fazio, Jonathan Foster, Peter Hudson, Nicola T. Lautenschlager, Nat Lenzo, Ralph N. Martins, Paul Maruff, Colin Masters, Andrew Milner, Kerryn Pike, Christopher Rowe, Greg Savage, Cassandra Szoeke, Kevin Taddei, Victor Villemagne, Michael Woodward, David Ames, and AIBL Research Group. 2009. The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: Methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer\u2019s disease. International Psychogeriatrics 21, 4 (2009), 672\u2013687.","journal-title":"International Psychogeriatrics"},{"key":"e_1_3_1_20_2","first-page":"343","article-title":"Adaptive spatial-spectral dictionary learning for hyperspectral image denoising","author":"Fu Ying","year":"2015","unstructured":"Ying Fu, Antony Lam, Imari Sato, and Yoichi Sato. 2015. Adaptive spatial-spectral dictionary learning for hyperspectral image denoising. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 343\u2013351.","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision"},{"issue":"1","key":"e_1_3_1_21_2","first-page":"92","article-title":"Laplacian sparse coding, hypergraph Laplacian sparse coding, and applications","volume":"35","author":"Gao Shenghua","year":"2012","unstructured":"Shenghua Gao, Ivor Wai-Hung Tsang, and Liang-Tien Chia. 2012. Laplacian sparse coding, hypergraph Laplacian sparse coding, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 1 (2012), 92\u2013104.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"12","key":"e_1_3_1_22_2","doi-asserted-by":"crossref","first-page":"5057","DOI":"10.1109\/TIP.2014.2362057","article-title":"Sparsity-based poisson denoising with dictionary learning","volume":"23","author":"Giryes Raja","year":"2014","unstructured":"Raja Giryes and Michael Elad. 2014. Sparsity-based poisson denoising with dictionary learning. IEEE Transactions on Image Processing 23, 12 (2014), 5057\u20135069.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"1","key":"e_1_3_1_23_2","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton Arthur","year":"2012","unstructured":"Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Sch\u00f6lkopf, and Alexander Smola. 2012. A kernel two-sample test. Journal of Machine Learning Research 13, 1 (2012), 723\u2013773.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_24_2","doi-asserted-by":"crossref","first-page":"2832","DOI":"10.1109\/TMI.2020.3046460","article-title":"Anatomic and molecular MR image synthesis using confidence guided CNNs","volume":"40","author":"Guo Pengfei","year":"2021","unstructured":"Pengfei Guo, Puyang Wang, Rajeev Yasarla, Jinyuan Zhou, Vishal M. Patel, and Shanshan Jiang. 2021. Anatomic and molecular MR image synthesis using confidence guided CNNs. IEEE Transactions on Medical Imaging 40 (2021), 2832\u20132844.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_25_2","first-page":"1","article-title":"Fast-FMI: Non-reference image fusion metric","author":"Haghighat Mohammad","year":"2014","unstructured":"Mohammad Haghighat and Masoud Amirkabiri Razian. 2014. Fast-FMI: Non-reference image fusion metric. In IEEE 8th International Conference on Application of Information and Communication Technologies (AICT \u201914), 1\u20133.","journal-title":"IEEE 8th International Conference on Application of Information and Communication Technologies (AICT \u201914)"},{"key":"e_1_3_1_26_2","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1002\/mp.12155","article-title":"MR-based synthetic CT generation using a deep convolutional neural network method","volume":"44","author":"Han Xiao","year":"2017","unstructured":"Xiao Han. 2017. MR-based synthetic CT generation using a deep convolutional neural network method. Medical Physics 44 (2017), 1408\u20131419.","journal-title":"Medical Physics"},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","first-page":"102136","DOI":"10.1016\/j.media.2021.102136","article-title":"Autoencoder based self-supervised test-time adaptation for medical image analysis","volume":"72","author":"He Yufan","year":"2021","unstructured":"Yufan He, Aaron Carass, Lianrui Zuo, Blake E. Dewey, and Jerry L. Prince. 2021. Autoencoder based self-supervised test-time adaptation for medical image analysis. Medical Image Analysis 72 (2021), 102136.","journal-title":"Medical Image Analysis"},{"key":"e_1_3_1_28_2","doi-asserted-by":"crossref","first-page":"105780","DOI":"10.1016\/j.compbiomed.2022.105780","article-title":"Nonfinite-modality data augmentation for brain image registration","volume":"147","author":"He Yuan","year":"2022","unstructured":"Yuan He, Aoyu Wang, Shuai Li, Yikang Yang, and Aimin Hao. 2022. Nonfinite-modality data augmentation for brain image registration. Computers in Biology and Medicine 147 (2022), 105780.","journal-title":"Computers in Biology and Medicine"},{"key":"e_1_3_1_29_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.neuroimage.2006.05.061","article-title":"Automatic anatomical brain MRI segmentation combining label propagation and decision fusion","volume":"33","author":"Heckemann Rolf A.","year":"2006","unstructured":"Rolf A. Heckemann, Joseph V. Hajnal, Paul Aljabar, Daniel Rueckert, and Alexander Hammers. 2006. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33 (2006), 115\u2013126.","journal-title":"Neuroimage"},{"key":"e_1_3_1_30_2","article-title":"Deep generative model for synthetic-CT generation with uncertainty predictions","author":"Hemsley Matt","year":"2020","unstructured":"Matt Hemsley, Brige P. Chugh, Mark Ruschin, Young Lee, Chia-Lin Tseng, Greg J. Stanisz, and Angus Z. Lau. 2020. Deep generative model for synthetic-CT generation with uncertainty predictions. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_31_2","article-title":"GANs trained by a two time-scale update rule converge to a local Nash equilibrium","volume":"30","author":"Heusel Martin","year":"2017","unstructured":"Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_32_2","article-title":"Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size","volume":"1803","author":"Hiasa Yuta","year":"2018","unstructured":"Yuta Hiasa, Yoshito Otake, Masaki Takao, Takumi Matsuoka, Kazuma Takashima, Jerry L. Prince, Nobuhiko Sugano, and Yoshinobu Sato. 2018. Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size. ArXiv abs\/1803.06629 (2018).","journal-title":"ArXiv"},{"key":"e_1_3_1_33_2","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1016\/j.neuroimage.2018.03.049","article-title":"The UNC\/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development","volume":"185","author":"Howell Brittany R.","year":"2019","unstructured":"Brittany R. Howell, Martin A. Styner, Wei Gao, Pew-Thian Yap, Li Wang, Kristine Baluyot, Essa Yacoub, Geng Chen, Taylor Potts, Andrew Salzwedel, et\u00a0al. 2019. The UNC\/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development. Neuroimage 185 (2019), 891\u2013905.","journal-title":"Neuroimage"},{"key":"e_1_3_1_34_2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/TMI.2021.3107013","article-title":"Bidirectional mapping generative adversarial networks for brain MR to PET synthesis","volume":"41","author":"Hu Shengye","year":"2022","unstructured":"Shengye Hu, Baiying Lei, Shuqiang Wang, Yong Wang, Zhiguang Feng, and Yanyan Shen. 2022. Bidirectional mapping generative adversarial networks for brain MR to PET synthesis. IEEE Transactions on Medical Imaging 41 (2022), 145\u2013157.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_35_2","article-title":"Brain MR to PET synthesis via bidirectional generative adversarial network","author":"Hu Shengye","year":"2020","unstructured":"Shengye Hu, Yanyan Shen, Shuqiang Wang, and Baiying Lei. 2020. Brain MR to PET synthesis via bidirectional generative adversarial network. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_36_2","first-page":"2496","article-title":"Coupled dictionary and feature space learning with applications to cross-domain image synthesis and recognition","author":"Huang De-An","year":"2013","unstructured":"De-An Huang and Yu-Chiang Frank Wang. 2013. Coupled dictionary and feature space learning with applications to cross-domain image synthesis and recognition. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2496\u20132503.","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision"},{"key":"e_1_3_1_37_2","article-title":"CoCa-GAN: Common-feature-learning-based context-aware generative adversarial network for glioma grading","author":"Huang Pu","year":"2019","unstructured":"Pu Huang, Dengwang Li, Zhicheng Jiao, Dongming Wei, Guoshi Li, Qian Wang, Han Zhang, and Dinggang Shen. 2019. CoCa-GAN: Common-feature-learning-based context-aware generative adversarial network for glioma grading. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_38_2","article-title":"Geometry regularized joint dictionary learning for cross-modality image synthesis in magnetic resonance imaging","author":"Huang Yawen","year":"2016","unstructured":"Yawen Huang, Leandro Beltrachini, Ling Shao, and Alejandro F. Frangi. 2016. Geometry regularized joint dictionary learning for cross-modality image synthesis in magnetic resonance imaging. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_39_2","article-title":"DOTE: Dual convolutional filter learning for super-resolution and cross-modality synthesis in MRI","author":"Huang Yawen","year":"2017","unstructured":"Yawen Huang, Ling Shao, and Alejandro F. Frangi. 2017. DOTE: Dual convolutional filter learning for super-resolution and cross-modality synthesis in MRI. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_40_2","first-page":"5787","article-title":"Simultaneous super-resolution and cross-modality synthesis of 3D medical images using weakly-supervised joint convolutional sparse coding","author":"Huang Yawen","year":"2017","unstructured":"Yawen Huang, Ling Shao, and Alejandro F. Frangi. 2017. Simultaneous super-resolution and cross-modality synthesis of 3D medical images using weakly-supervised joint convolutional sparse coding. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 5787\u20135796.","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1109\/TMI.2017.2781192","article-title":"Cross-modality image synthesis via weakly coupled and geometry co-regularized joint dictionary learning","volume":"37","author":"Huang Yawen","year":"2018","unstructured":"Yawen Huang, Ling Shao, and Alejandro F. Frangi. 2018. Cross-modality image synthesis via weakly coupled and geometry co-regularized joint dictionary learning. IEEE Transactions on Medical Imaging 37 (2018), 815\u2013827.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_42_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-13969-8_21","article-title":"Simultaneous super-resolution and cross-modality synthesis in magnetic resonance imaging","author":"Huang Yawen","year":"2019","unstructured":"Yawen Huang, Ling Shao, and Alejandro F. Frangi. 2019. Simultaneous super-resolution and cross-modality synthesis in magnetic resonance imaging. In Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.","journal-title":"Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics"},{"key":"e_1_3_1_43_2","doi-asserted-by":"crossref","first-page":"8187","DOI":"10.1109\/TIP.2020.3011557","article-title":"MCMT-GAN: Multi-task coherent modality transferable GAN for 3D brain image synthesis","volume":"29","author":"Huang Yawen","year":"2020","unstructured":"Yawen Huang, Feng Zheng, Runmin Cong, Weilin Huang, Matthew R. Scott, and Ling Shao. 2020. MCMT-GAN: Multi-task coherent modality transferable GAN for 3D brain image synthesis. IEEE Transactions on Image Processing 29 (2020), 8187\u20138198.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_3_1_44_2","article-title":"Super-resolution and inpainting with degraded and upgraded generative adversarial networks","author":"Huang Yawen","year":"2020","unstructured":"Yawen Huang, Feng Zheng, Danyang Wang, Junyu Jiang, Xiaoqian Wang, and Ling Shao. 2020. Super-resolution and inpainting with degraded and upgraded generative adversarial networks. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_45_2","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1109\/TMI.2018.2876633","article-title":"SynSeg-Net: Synthetic segmentation without target modality ground truth","volume":"38","author":"Huo Yuankai","year":"2019","unstructured":"Yuankai Huo, Zhoubing Xu, Hyeonsoo Moon, Shunxing Bao, Albert Assad, Tamara K. Moyo, Michael R. Savona, Richard G. Abramson, and Bennett A. Landman. 2019. SynSeg-Net: Synthetic segmentation without target modality ground truth. IEEE Transactions on Medical Imaging 38 (2019), 1016\u20131025.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"e_1_3_1_46_2","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/TMI.2015.2461533","article-title":"Estimating CT image from MRI data using structured random forest and auto-context model","volume":"35","author":"Huynh Tri","year":"2015","unstructured":"Tri Huynh, Yaozong Gao, Jiayin Kang, Li Wang, Pei Zhang, Jun Lian, and Dinggang Shen. 2015. Estimating CT image from MRI data using structured random forest and auto-context model. IEEE Transactions on Medical Imaging 35, 1 (2015), 174\u2013183.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_47_2","first-page":"5967","article-title":"Image-to-image translation with conditional adversarial networks","author":"Isola Phillip","year":"2017","unstructured":"Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. 2017. Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 5967\u20135976.","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"issue":"4","key":"e_1_3_1_48_2","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1002\/jmri.21049","article-title":"The Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI): MRI methods","volume":"27","author":"Jr. Clifford R. Jack","year":"2008","unstructured":"Clifford R. Jack Jr., Matt A. Bernstein, Nick C. Fox, Paul Thompson, Gene Alexander, Danielle Harvey, Bret Borowski, Paula J. Britson, Jennifer L. Whitwell, Chadwick Ward, et\u00a0al. 2008. The Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine 27, 4 (2008), 685\u2013691.","journal-title":"Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine"},{"key":"e_1_3_1_49_2","doi-asserted-by":"crossref","first-page":"4413","DOI":"10.1109\/TMI.2020.3018560","article-title":"Self-supervised ultrasound to MRI fetal brain image synthesis","volume":"39","author":"Jiao Jianbo","year":"2020","unstructured":"Jianbo Jiao, Ana I. L. Namburete, Aris T. Papageorghiou, and Julia Alison Noble. 2020. Self-supervised ultrasound to MRI fetal brain image synthesis. IEEE Transactions on Medical Imaging 39 (2020), 4413\u20134424.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_50_2","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.media.2016.08.009","article-title":"Random forest regression for magnetic resonance image synthesis","volume":"35","author":"Jog Amod","year":"2017","unstructured":"Amod Jog, Aaron Carass, Snehashis Roy, Dzung L. Pham, and Jerry L. Prince. 2017. Random forest regression for magnetic resonance image synthesis. Medical Image Analysis 35 (2017), 475\u2013488.","journal-title":"Medical Image Analysis"},{"key":"e_1_3_1_51_2","article-title":"Robust multi-modal MR image synthesis","author":"Joyce Thomas","year":"2017","unstructured":"Thomas Joyce, Agisilaos Chartsias, and Sotirios A. Tsaftaris. 2017. Robust multi-modal MR image synthesis. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_52_2","first-page":"402","article-title":"Demystifying T1-MRI to FDG \\(^{18}\\) -PET image translation via representational similarity","author":"Kao Chia-Hsiang","year":"2021","unstructured":"Chia-Hsiang Kao, Yong-Sheng Chen, Li-Fen Chen, and Wei-Chen Chiu. 2021. Demystifying T1-MRI to FDG \\(^{18}\\) -PET image translation via representational similarity. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 402\u2013412.","journal-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"e_1_3_1_53_2","article-title":"Imitation learning for improved 3D PET\/MR attenuation correction","volume":"71","author":"Kl\u00e4ser Kerstin","year":"2021","unstructured":"Kerstin Kl\u00e4ser, Thomas Varsavsky, Pawel J. Markiewicz, Tom Kamiel Magda Vercauteren, Alexander Hammers, David Atkinson, K. Thielemans, Brian F. Hutton, Manuel Jorge Cardoso, and S\u00e9bastien Ourselin. 2021. Imitation learning for improved 3D PET\/MR attenuation correction. Medical Image Analysis 71 (2021), 102079.","journal-title":"Medical Image Analysis"},{"issue":"3","key":"e_1_3_1_54_2","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/j.neuroimage.2008.12.037","article-title":"Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration","volume":"46","author":"Klein Arno","year":"2009","unstructured":"Arno Klein, Jesper Andersson, Babak A. Ardekani, John Ashburner, Brian Avants, Ming-Chang Chiang, Gary E. Christensen, D. Louis Collins, James Gee, Pierre Hellier, et\u00a0al. 2009. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46, 3 (2009), 786\u2013802.","journal-title":"Neuroimage"},{"key":"e_1_3_1_55_2","first-page":"1964","article-title":"Breaking the dilemma of medical image-to-image translation","volume":"34","author":"Kong Lingke","year":"2021","unstructured":"Lingke Kong, Chenyu Lian, Detian Huang, Zhenjiang Li, Yanle Hu, and Qichao Zhou. 2021. Breaking the dilemma of medical image-to-image translation. Advances in Neural Information Processing Systems 34 (2021), 1964\u20131978.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_56_2","article-title":"Generation of 3D brain MRI using auto-encoding generative adversarial networks","author":"Kwon Gihyun","year":"2019","unstructured":"Gihyun Kwon, Chihye Han, and Dae-Shik Kim. 2019. Generation of 3D brain MRI using auto-encoding generative adversarial networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"issue":"4","key":"e_1_3_1_57_2","doi-asserted-by":"crossref","first-page":"2854","DOI":"10.1016\/j.neuroimage.2010.11.047","article-title":"Multi-parametric neuroimaging reproducibility: A 3-T resource study","volume":"54","author":"Landman Bennett A.","year":"2011","unstructured":"Bennett A. Landman, Alan J. Huang, Aliya Gifford, Deepti S. Vikram, Issel Anne L. Lim, Jonathan A. D. Farrell, John A. Bogovic, Jun Hua, Min Chen, Samson Jarso, et\u00a0al. 2011. Multi-parametric neuroimaging reproducibility: A 3-T resource study. Neuroimage 54, 4 (2011), 2854\u20132866.","journal-title":"Neuroimage"},{"key":"e_1_3_1_58_2","first-page":"2482","article-title":"CollaGAN: Collaborative GAN for missing image data imputation","author":"Lee Dongwook","year":"2019","unstructured":"Dongwook Lee, Junyoung Kim, Won-Jin Moon, and J. C. Ye. 2019. CollaGAN: Collaborative GAN for missing image data imputation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2482\u20132491.","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_1_59_2","article-title":"DiamondGAN: Unified multi-modal generative adversarial networks for MRI sequences synthesis","volume":"1904","author":"Li Hongwei","year":"2019","unstructured":"Hongwei Li, Johannes C. Paetzold, Anjany Kumar Sekuboyina, Florian Kofler, Jianguo Zhang, Jan S. Kirschke, Benedikt Wiestler, and Bjoern H. Menze. 2019. DiamondGAN: Unified multi-modal generative adversarial networks for MRI sequences synthesis. ArXiv abs\/1904.12894 (2019).","journal-title":"ArXiv"},{"issue":"2","key":"e_1_3_1_60_2","first-page":"278","article-title":"A locality-constrained and label embedding dictionary learning algorithm for image classification","volume":"28","author":"Li Zhengming","year":"2015","unstructured":"Zhengming Li, Zhihui Lai, Yong Xu, Jian Yang, and David Zhang. 2015. A locality-constrained and label embedding dictionary learning algorithm for image classification. IEEE Transactions on Neural Networks and Learning Systems 28, 2 (2015), 278\u2013293.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"1","key":"e_1_3_1_61_2","first-page":"1","article-title":"A large, open source dataset of stroke anatomical brain images and manual lesion segmentations","volume":"5","author":"Liew Sook-Lei","year":"2018","unstructured":"Sook-Lei Liew, Julia M. Anglin, Nick W. Banks, Matt Sondag, Kaori L. Ito, Hosung Kim, Jennifer Chan, Joyce Ito, Connie Jung, Nima Khoshab, et\u00a0al. 2018. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific Data 5, 1 (2018), 1\u201311.","journal-title":"Scientific Data"},{"issue":"5","key":"e_1_3_1_62_2","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1109\/TMI.2014.2303821","article-title":"Computer-aided detection of prostate cancer in MRI","volume":"33","author":"Litjens Geert","year":"2014","unstructured":"Geert Litjens, Oscar Debats, Jelle Barentsz, Nico Karssemeijer, and Henkjan Huisman. 2014. Computer-aided detection of prostate cancer in MRI. IEEE Transactions on Medical Imaging 33, 5 (2014), 1083\u20131092.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_63_2","first-page":"10","article-title":"A unified conditional disentanglement framework for multimodal brain MR image translation","author":"Liu Xiaofeng","year":"2021","unstructured":"Xiaofeng Liu, Fangxu Xing, Georges El Fakhri, and Jonghye Woo. 2021. A unified conditional disentanglement framework for multimodal brain MR image translation. In IEEE 18th International Symposium on Biomedical Imaging, 10\u201314.","journal-title":"IEEE 18th International Symposium on Biomedical Imaging"},{"key":"e_1_3_1_64_2","doi-asserted-by":"crossref","first-page":"102266","DOI":"10.1016\/j.media.2021.102266","article-title":"Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages","volume":"75","author":"Liu Yunbi","year":"2022","unstructured":"Yunbi Liu, Ling Yue, Shifu Xiao, Wei Yang, Dinggang Shen, and Mingxia Liu. 2022. Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages. Medical Image Analysis 75 (2022), 102266.","journal-title":"Medical Image Analysis"},{"key":"e_1_3_1_65_2","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1109\/42.563664","article-title":"Multimodality image registration by maximization of mutual information","volume":"16","author":"Maes Frederik","year":"1997","unstructured":"Frederik Maes, Andr\u00e9 M. F. Collignon, Dirk Vandermeulen, Guy Marchal, and Paul Suetens. 1997. Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16 (1997), 187\u2013198.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_66_2","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.media.2016.07.009","article-title":"ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI","volume":"35","author":"Maier Oskar","year":"2017","unstructured":"Oskar Maier, Bjoern H. Menze, Janina von der Gablentz, Levin H\u00e4ni, Mattias P. Heinrich, Matthias Liebrand, Stefan Winzeck, Abdul Basit, Paul Bentley, Liang Chen, et\u00a0al. 2017. ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Medical Image Analysis 35 (2017), 250\u2013269.","journal-title":"Medical Image Analysis"},{"issue":"2","key":"e_1_3_1_67_2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1561\/0600000058","article-title":"Sparse modeling for image and vision processing","volume":"8","author":"Mairal Julien","year":"2014","unstructured":"Julien Mairal, Francis Bach, Jean Ponce, et\u00a0al. 2014. Sparse modeling for image and vision processing. Foundations and Trends\u00ae in Computer Graphics and Vision 8, 2\u20133 (2014), 85\u2013283.","journal-title":"Foundations and Trends\u00ae in Computer Graphics and Vision"},{"key":"e_1_3_1_68_2","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/aada6d","article-title":"Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy","volume":"63","author":"Maspero M.","year":"2018","unstructured":"M. Maspero, Mark Savenije, Anna M. Dinkla, Peter R. Seevinck, Martijn P. W. Intven, Ina M. Jurgenliemk-Schulz, Linda G. W. Kerkmeijer, and Cornelis A. T. van den Berg. 2018. Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy. Physics in Medicine & Biology 63 (2018), 185001.","journal-title":"Physics in Medicine & Biology"},{"issue":"2","key":"e_1_3_1_69_2","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","article-title":"Comparison of the predicted and observed secondary structure of T4 phage lysozyme","volume":"405","author":"Matthews Brian W.","year":"1975","unstructured":"Brian W. Matthews. 1975. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure 405, 2 (1975), 442\u2013451.","journal-title":"Biochimica et Biophysica Acta (BBA)-Protein Structure"},{"key":"e_1_3_1_70_2","article-title":"MRBrainS challenge: Online evaluation framework for brain image segmentation in 3T MRI scans","volume":"2015","author":"Mendrik Adri\u00ebnne M.","year":"2015","unstructured":"Adri\u00ebnne M. Mendrik, Koen L. Vincken, Hugo J. Kuijf, Marcel Breeuwer, Willem H. Bouvy, Jeroen De Bresser, Amir Alansary, Marleen De Bruijne, Aaron Carass, Ayman El-Baz, et\u00a0al. 2015. MRBrainS challenge: Online evaluation framework for brain image segmentation in 3T MRI scans. Computational Intelligence and Neuroscience 2015 (2015), 1\u20131.","journal-title":"Computational Intelligence and Neuroscience"},{"issue":"10","key":"e_1_3_1_71_2","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The multimodal brain tumor image segmentation benchmark (BRATS)","volume":"34","author":"Menze Bjoern H.","year":"2014","unstructured":"Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, et\u00a0al. 2014. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging 34, 10 (2014), 1993\u20132024.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_72_2","first-page":"565","article-title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari Fausto","year":"2016","unstructured":"Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. 2016. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In The 4th International Conference on 3D Vision (3DV \u201916), 565\u2013571.","journal-title":"The 4th International Conference on 3D Vision (3DV \u201916)"},{"key":"e_1_3_1_73_2","article-title":"Cross-domain synthesis of medical images using efficient location-sensitive deep network","author":"Nguyen Hien Van","year":"2015","unstructured":"Hien Van Nguyen, Shaohua Kevin Zhou, and Raviteja Vemulapalli. 2015. Cross-domain synthesis of medical images using efficient location-sensitive deep network. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_74_2","first-page":"417","article-title":"Medical image synthesis with context-aware generative adversarial networks","volume":"10435","author":"Nie Dong","year":"2017","unstructured":"Dong Nie, Roger Trullo, Caroline Petitjean, Su Ruan, and Dinggang Shen. 2017. Medical image synthesis with context-aware generative adversarial networks. International Conference on Medical Image Computing and Computer-Assisted Intervention 10435 (2017), 417\u2013425.","journal-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"e_1_3_1_75_2","article-title":"Generative adversarial training for MRA image synthesis using multi-contrast MRI","author":"Olut Sahin","year":"2018","unstructured":"Sahin Olut, Yusuf Huseyin Sahin, Ugur Demir, and G\u00f6zde B. \u00dcnal. 2018. Generative adversarial training for MRA image synthesis using multi-contrast MRI. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_76_2","article-title":"Disease-image specific generative adversarial network for brain disease diagnosis with incomplete multi-modal neuroimages","author":"Pan Yongsheng","year":"2019","unstructured":"Yongsheng Pan, Mingxia Liu, Chunfeng Lian, Yong Xia, and Dinggang Shen. 2019. Disease-image specific generative adversarial network for brain disease diagnosis with incomplete multi-modal neuroimages. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_77_2","first-page":"455","article-title":"Synthesizing missing PET from MRI with cycle-consistent generative adversarial networks for Alzheimer\u2019s disease diagnosis","volume":"11072","author":"Pan Yongsheng","year":"2018","unstructured":"Yongsheng Pan, Mingxia Liu, Chunfeng Lian, Tao Zhou, Yong Xia, and Dinggang Shen. 2018. Synthesizing missing PET from MRI with cycle-consistent generative adversarial networks for Alzheimer\u2019s disease diagnosis. International Conference on Medical Image Computing and Computer-Assisted Intervention 11072 (2018), 455\u2013463.","journal-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"e_1_3_1_78_2","article-title":"Disease-image-specific learning for diagnosis-oriented neuroimage synthesis with incomplete multi-modality data.","author":"Pan Yongsheng","year":"2021","unstructured":"Yongsheng Pan, Mingxia Liu, Yong Xia, and Dinggang Shen. 2021. Disease-image-specific learning for diagnosis-oriented neuroimage synthesis with incomplete multi-modality data. IEEE Transactions on Pattern Analysis and Machine Intelligence 10 (2021), 6839\u20136853.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_79_2","first-page":"III\u2013173","article-title":"A new quality metric for image fusion","volume":"3","author":"Piella Gemma","year":"2003","unstructured":"Gemma Piella and Henk Heijmans. 2003. A new quality metric for image fusion. In Proceedings 2003 International Conference on Image Processing (Cat. No. 03CH37429) 3, III\u2013173.","journal-title":"Proceedings 2003 International Conference on Image Processing (Cat. No. 03CH37429)"},{"key":"e_1_3_1_80_2","doi-asserted-by":"crossref","first-page":"5343","DOI":"10.1109\/TIP.2022.3195059","article-title":"Joint specifics and consistency hash learning for large-scale cross-modal retrieval","volume":"31","author":"Qin Jianyang","year":"2022","unstructured":"Jianyang Qin, Lunke Fei, Zheng Zhang, Jie Wen, Yong Xu, and David Zhang. 2022. Joint specifics and consistency hash learning for large-scale cross-modal retrieval. IEEE Transactions on Image Processing 31 (2022), 5343\u20135358.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_3_1_81_2","first-page":"352","article-title":"Multimodal brain MRI translation focused on lesions","author":"Qu Yili","year":"2020","unstructured":"Yili Qu, Chufu Deng, Wanqi Su, Ying Wang, Yutong Lu, and Zhiguang Chen. 2020. Multimodal brain MRI translation focused on lesions. In Proceedings of the 12th International Conference on Machine Learning and Computing, 352\u2013359.","journal-title":"Proceedings of the 12th International Conference on Machine Learning and Computing"},{"key":"e_1_3_1_82_2","doi-asserted-by":"crossref","first-page":"2348","DOI":"10.1109\/TMI.2013.2282126","article-title":"Magnetic resonance image example-based contrast synthesis","volume":"32","author":"Roy Snehashis","year":"2013","unstructured":"Snehashis Roy, Aaron Carass, and Jerry L. Prince. 2013. Magnetic resonance image example-based contrast synthesis. IEEE Transactions on Medical Imaging 32 (2013), 2348\u20132363.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"6","key":"e_1_3_1_83_2","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1109\/JPROC.2010.2040551","article-title":"Dictionaries for sparse representation modeling","volume":"98","author":"Rubinstein Ron","year":"2010","unstructured":"Ron Rubinstein, Alfred M. Bruckstein, and Michael Elad. 2010. Dictionaries for sparse representation modeling. Proceedings of the IEEE 98, 6 (2010), 1045\u20131057.","journal-title":"Proceedings of the IEEE"},{"issue":"8","key":"e_1_3_1_84_2","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 Daniel","year":"1999","unstructured":"Daniel Rueckert, Luke I. Sonoda, Carmel Hayes, Derek L. G. Hill, Martin O. Leach, and David J. Hawkes. 1999. Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging 18, 8 (1999), 712\u2013721.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_85_2","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju Ramprasaath R.","year":"2019","unstructured":"Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra. 2019. Grad-CAM: Visual explanations from deep networks via gradient-based localization. International Journal of Computer Vision 128 (2019), 336\u2013359.","journal-title":"International Journal of Computer Vision"},{"key":"e_1_3_1_86_2","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1109\/TMI.2019.2945521","article-title":"Missing MRI pulse sequence synthesis using multi-modal generative adversarial network","volume":"39","author":"Sharma Anmol","year":"2020","unstructured":"Anmol Sharma and G. Hamarneh. 2020. Missing MRI pulse sequence synthesis using multi-modal generative adversarial network. IEEE Transactions on Medical Imaging 39 (2020), 1170\u20131183.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_87_2","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1109\/TMI.2020.3046444","article-title":"Multi-domain image completion for random missing input data","volume":"40","author":"Shen Liyue","year":"2021","unstructured":"Liyue Shen, Wentao Zhu, Xiaosong Wang, Lei Xing, John M. Pauly, Baris Turkbey, Stephanie A. Harmon, Thomas Sanford, Sherif Mehralivand, Peter L. Choyke, Bradford J. Wood, and Daguang Xu. 2021. Multi-domain image completion for random missing input data. IEEE Transactions on Medical Imaging 40 (2021), 1113\u20131122.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_88_2","article-title":"GANDALF: Generative adversarial networks with discriminator-adaptive loss fine-tuning for Alzheimer\u2019s disease diagnosis from MRI","author":"Shin Hoo-Chang","year":"2020","unstructured":"Hoo-Chang Shin, Alvin Ihsani, Ziyue Xu, Swetha Mandava, Sharath Turuvekere Sreenivas, Christopher Forster, Jiook Cha, and Alzheimer\u2019s Disease Neuroimaging Initiative. 2020. GANDALF: Generative adversarial networks with discriminator-adaptive loss fine-tuning for Alzheimer\u2019s disease diagnosis from MRI. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_89_2","first-page":"191","article-title":"Learning fixed points in generative adversarial networks: From image-to-image translation to disease detection and localization","author":"Siddiquee Md Mahfuzur Rahman","year":"2019","unstructured":"Md Mahfuzur Rahman Siddiquee, Zongwei Zhou, Nima Tajbakhsh, Ruibin Feng, Michael B. Gotway, Yoshua Bengio, and Jianming Liang. 2019. Learning fixed points in generative adversarial networks: From image-to-image translation to disease detection and localization. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 191\u2013200.","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision"},{"key":"e_1_3_1_90_2","article-title":"Cancer statistics, 2019","volume":"69","author":"Siegel Rebecca L.","year":"2019","unstructured":"Rebecca L. Siegel, Kimberly D. Miller, and Ahmedin Jemal. 2019. Cancer statistics, 2019. CA: A Cancer Journal for Clinicians 69 (2019), 7\u201334.","journal-title":"CA: A Cancer Journal for Clinicians"},{"key":"e_1_3_1_91_2","doi-asserted-by":"crossref","unstructured":"Carole H. Sudre Wenqi Li Tom Vercauteren Sebastien Ourselin and M. Jorge Cardoso. 2017. Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations . Springer 240\u2013248.","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"e_1_3_1_92_2","doi-asserted-by":"crossref","first-page":"2303","DOI":"10.1109\/JBHI.2020.2964016","article-title":"An adversarial learning approach to medical image synthesis for lesion detection","volume":"24","author":"Sun Liyan","year":"2020","unstructured":"Liyan Sun, Jiexiang Wang, Yue Huang, Xinghao Ding, Hayit Greenspan, and John William Paisley. 2020. An adversarial learning approach to medical image synthesis for lesion detection. IEEE Journal of Biomedical and Health Informatics 24 (2020), 2303\u20132314.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_3_1_93_2","doi-asserted-by":"crossref","first-page":"2926","DOI":"10.1109\/TMI.2021.3059265","article-title":"Self-attentive spatial adaptive normalization for cross-modality domain adaptation","volume":"40","author":"Tomar Devavrat","year":"2021","unstructured":"Devavrat Tomar, Manana Lortkipanidze, Guillaume Vray, Behzad Bozorgtabar, and Jean-Philippe Thiran. 2021. Self-attentive spatial adaptive normalization for cross-modality domain adaptation. IEEE Transactions on Medical Imaging 40 (2021), 2926\u20132938.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_94_2","article-title":"Texture networks: Feed-forward synthesis of textures and stylized images","author":"Ulyanov Dmitry","year":"2016","unstructured":"Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, and Victor Lempitsky. 2016. Texture networks: Feed-forward synthesis of textures and stylized images. arXiv preprint arXiv:1603.03417 (2016).","journal-title":"arXiv preprint arXiv:1603.03417"},{"key":"e_1_3_1_95_2","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","article-title":"The WU-Minn human connectome project: An overview","volume":"80","author":"Essen David C. Van","year":"2013","unstructured":"David C. Van Essen, Stephen M. Smith, Deanna M. Barch, Timothy E. J. Behrens, Essa Yacoub, Kamil Ugurbil, Wu-Minn H. C. P. Consortium, et\u00a0al. 2013. The WU-Minn human connectome project: An overview. Neuroimage 80 (2013), 62\u201379.","journal-title":"Neuroimage"},{"key":"e_1_3_1_96_2","first-page":"1096","article-title":"Extracting and composing robust features with denoising autoencoders","author":"Vincent Pascal","year":"2008","unstructured":"Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning, 1096\u20131103.","journal-title":"Proceedings of the 25th International Conference on Machine Learning"},{"key":"e_1_3_1_97_2","article-title":"Region-enhanced joint dictionary learning for cross-modality synthesis in diffusion tensor Imaging","author":"Wang Danyang","year":"2017","unstructured":"Danyang Wang, Yawen Huang, and Alejandro F. Frangi. 2017. Region-enhanced joint dictionary learning for cross-modality synthesis in diffusion tensor Imaging. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_98_2","doi-asserted-by":"crossref","first-page":"126282","DOI":"10.1016\/j.neucom.2023.126282","article-title":"FedMed-GAN: Federated domain translation on unsupervised cross-modality brain image synthesis","volume":"546","author":"Wang Jinbao","year":"2023","unstructured":"Jinbao Wang, Guoyang Xie, Yawen Huang, Jiayi Lyu, Feng Zheng, Yefeng Zheng, and Yaochu Jin. 2023. FedMed-GAN: Federated domain translation on unsupervised cross-modality brain image synthesis. Neurocomputing 546 (2023), 126282.","journal-title":"Neurocomputing"},{"key":"e_1_3_1_99_2","article-title":"FedMed-ATL: Misaligned unpaired cross-modality neuroimage synthesis via affine transform loss","author":"Wang Jinbao","year":"2022","unstructured":"Jinbao Wang, Guoyang Xie, Yawen Huang, Yefeng Zheng, Yaochu Jin, and Feng Zheng. 2022. FedMed-ATL: Misaligned unpaired cross-modality neuroimage synthesis via affine transform loss. In Proceedings of the 30th ACM International Conference on Multimedia.","journal-title":"Proceedings of the 30th ACM International Conference on Multimedia"},{"key":"e_1_3_1_100_2","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.neuroimage.2014.12.042","article-title":"LINKS: Learning-based multi-source integration framework for segmentation of infant brain images","volume":"108","author":"Wang Li","year":"2015","unstructured":"Li Wang, Yaozong Gao, Feng Shi, Gang Li, John H. Gilmore, Weili Lin, and Dinggang Shen. 2015. LINKS: Learning-based multi-source integration framework for segmentation of infant brain images. Neuroimage 108 (2015), 160\u2013172.","journal-title":"Neuroimage"},{"key":"e_1_3_1_101_2","first-page":"2216","article-title":"Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis","author":"Wang Shenlong","year":"2012","unstructured":"Shenlong Wang, Lei Zhang, Yan Liang, and Quan Pan. 2012. Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2216\u20132223.","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_1_102_2","first-page":"329","article-title":"Locality adaptive multi-modality GANs for high-quality PET image synthesis","volume":"11070","author":"Wang Yan","year":"2018","unstructured":"Yan Wang, Luping Zhou, Lei Wang, Biting Yu, Chen Zu, David S. Lalush, Weili Lin, Xi Wu, Jiliu Zhou, and Dinggang Shen. 2018. Locality adaptive multi-modality GANs for high-quality PET image synthesis. International Conference on Medical Image Computing and Computer-assisted Intervention 11070 (2018), 329\u2013337.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_103_2","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TMI.2018.2884053","article-title":"3D auto-context-based locality adaptive multi-modality GANs for PET synthesis","volume":"38","author":"Wang Yan","year":"2019","unstructured":"Yan Wang, Luping Zhou, Biting Yu, Lei Wang, Chen Zu, David S. Lalush, Weili Lin, Xi Wu, Jiliu Zhou, and Dinggang Shen. 2019. 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis. IEEE Transactions on Medical Imaging 38 (2019), 1328\u20131339.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"3","key":"e_1_3_1_104_2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/97.995823","article-title":"A universal image quality index","volume":"9","author":"Wang Zhou","year":"2002","unstructured":"Zhou Wang and Alan C. Bovik. 2002. A universal image quality index. IEEE Signal Processing Letters 9, 3 (2002), 81\u201384.","journal-title":"IEEE Signal Processing Letters"},{"issue":"4","key":"e_1_3_1_105_2","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 Zhou","year":"2004","unstructured":"Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13, 4 (2004), 600\u2013612.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_3_1_106_2","article-title":"Learning myelin content in multiple sclerosis from multimodal MRI through adversarial training","author":"Wei Wen","year":"2018","unstructured":"Wen Wei, Emilie Poirion, Benedetta Bodini, Stanley Durrleman, Nicholas Ayache, Bruno Stankoff, and Olivier Colliot. 2018. Learning myelin content in multiple sclerosis from multimodal MRI through adversarial training. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"issue":"4","key":"e_1_3_1_107_2","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1097\/00004728-199707000-00007","article-title":"Comparison and evaluation of retrospective intermodality brain image registration techniques","volume":"21","author":"West Jay","year":"1997","unstructured":"Jay West, J. Michael Fitzpatrick, Matthew Y. Wang, Benoit M. Dawant, Calvin R. Maurer Jr., Robert M. Kessler, Robert J. Maciunas, Christian Barillot, Didier Lemoine, Andre Collignon, et\u00a0al. 1997. Comparison and evaluation of retrospective intermodality brain image registration techniques. Journal of Computer Assisted Tomography 21, 4 (1997), 554\u2013568.","journal-title":"Journal of Computer Assisted Tomography"},{"key":"e_1_3_1_108_2","first-page":"1803","article-title":"Multi-modality generative adversarial networks with tumor consistency loss for brain MR image synthesis","author":"Xin Bingyu","year":"2020","unstructured":"Bingyu Xin, Yifan Hu, Yefeng Zheng, and Hongen Liao. 2020. Multi-modality generative adversarial networks with tumor consistency loss for brain MR image synthesis. In IEEE 17th International Symposium on Biomedical Imaging, 1803\u20131807.","journal-title":"IEEE 17th International Symposium on Biomedical Imaging"},{"key":"e_1_3_1_109_2","doi-asserted-by":"crossref","first-page":"4249","DOI":"10.1109\/TMI.2020.3015379","article-title":"Unsupervised MR-to-CT synthesis using structure-constrained CycleGAN","volume":"39","author":"Yang Heran","year":"2020","unstructured":"Heran Yang, Jian Sun, Aaron Carass, Can Zhao, Junghoon Lee, Jerry L. Prince, and Zongben Xu. 2020. Unsupervised MR-to-CT synthesis using structure-constrained CycleGAN. IEEE Transactions on Medical Imaging 39 (2020), 4249\u20134261.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_110_2","article-title":"A unified hyper-GAN model for unpaired multi-contrast MR Image translation","author":"Yang Heran","year":"2021","unstructured":"Heran Yang, Jian Sun, Liwei Yang, and Zongben Xu. 2021. A unified hyper-GAN model for unpaired multi-contrast MR Image translation. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"issue":"8","key":"e_1_3_1_111_2","doi-asserted-by":"crossref","first-page":"3467","DOI":"10.1109\/TIP.2012.2192127","article-title":"Coupled dictionary training for image super-resolution","volume":"21","author":"Yang Jianchao","year":"2012","unstructured":"Jianchao Yang, Zhaowen Wang, Zhe Lin, Scott Cohen, and Thomas Huang. 2012. Coupled dictionary training for image super-resolution. IEEE Transactions on Image Processing 21, 8 (2012), 3467\u20133478.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"11","key":"e_1_3_1_112_2","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","article-title":"Image super-resolution via sparse representation","volume":"19","author":"Yang Jianchao","year":"2010","unstructured":"Jianchao Yang, John Wright, Thomas S. Huang, and Yi Ma. 2010. Image super-resolution via sparse representation. IEEE Transactions on Image Processing 19, 11 (2010), 2861\u20132873.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_3_1_113_2","article-title":"MRI image-to-image translation for cross-modality image registration and segmentation","author":"Yang Qianye","year":"2018","unstructured":"Qianye Yang, Nannan Li, Zixu Zhao, Xingyu Fan, E. C. Chang, Yan Xu, et\u00a0al. 2018. MRI image-to-image translation for cross-modality image registration and segmentation. arXiv preprint arXiv:1801.06940 (2018).","journal-title":"arXiv preprint arXiv:1801.06940"},{"issue":"1","key":"e_1_3_1_114_2","first-page":"1","article-title":"MRI cross-modality image-to-image translation","volume":"10","author":"Yang Qianye","year":"2020","unstructured":"Qianye Yang, Nannan Li, Zixu Zhao, Xingyu Fan, Eric I. Chang, Yan Xu, et\u00a0al. 2020. MRI cross-modality image-to-image translation. Scientific Reports 10, 1 (2020), 1\u201318.","journal-title":"Scientific Reports"},{"key":"e_1_3_1_115_2","first-page":"606","article-title":"Modality propagation: Coherent synthesis of subject-specific scans with data-driven regularization","volume":"16","author":"Ye Dong Hye","year":"2013","unstructured":"Dong Hye Ye, Darko Zikic, Ben Glocker, Antonio Criminisi, and Ender Konukoglu. 2013. Modality propagation: Coherent synthesis of subject-specific scans with data-driven regularization. International Conference on Medical Image Computing and Computer-assisted Intervention 16 Pt 1 (2013), 606\u201313.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_116_2","doi-asserted-by":"crossref","first-page":"101552","DOI":"10.1016\/j.media.2019.101552","article-title":"Generative adversarial network in medical imaging: A review","volume":"58","author":"Yi Xin","year":"2019","unstructured":"Xin Yi, Ekta Walia, and Paul S. Babyn. 2019. Generative adversarial network in medical imaging: A review. Medical Image Analysis 58 (2019), 101552.","journal-title":"Medical Image Analysis"},{"key":"e_1_3_1_117_2","first-page":"626","article-title":"3D cGAN based cross-modality MR image synthesis for brain tumor segmentation","author":"Yu Biting","year":"2018","unstructured":"Biting Yu, Luping Zhou, Lei Wang, Jurgen Fripp, and Pierrick T. Bourgeat. 2018. 3D cGAN based cross-modality MR image synthesis for brain tumor segmentation. In 2018 IEEE 15th International Symposium on Biomedical Imaging, 626\u2013630.","journal-title":"2018 IEEE 15th International Symposium on Biomedical Imaging"},{"key":"e_1_3_1_118_2","doi-asserted-by":"crossref","first-page":"2339","DOI":"10.1109\/TMI.2020.2969630","article-title":"Sample-adaptive GANs: Linking global and local mappings for cross-modality MR image synthesis","volume":"39","author":"Yu Biting","year":"2020","unstructured":"Biting Yu, Luping Zhou, Lei Wang, Yinghuan Shi, Jurgen Fripp, and Pierrick T. Bourgeat. 2020. Sample-adaptive GANs: Linking global and local mappings for cross-modality MR image synthesis. IEEE Transactions on Medical Imaging 39 (2020), 2339\u20132350.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_119_2","article-title":"MouseGAN: GAN-based multiple MRI modalities synthesis and segmentation for mouse brain structures","author":"Yu Ziqi","year":"2021","unstructured":"Ziqi Yu, Yuting Zhai, Xiaoyang Han, Tingying Peng, and Xiao-Yong Zhang. 2021. MouseGAN: GAN-based multiple MRI modalities synthesis and segmentation for mouse brain structures. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_120_2","doi-asserted-by":"crossref","first-page":"101944","DOI":"10.1016\/j.media.2020.101944","article-title":"mustGAN: Multi-stream generative adversarial networks for MR image synthesis","volume":"70","author":"Yurt Mahmut","year":"2021","unstructured":"Mahmut Yurt, Salman Ul Hassan Dar, Aykut Erdem, Erkut Erdem, and Tolga \u00c7ukur. 2021. mustGAN: Multi-stream generative adversarial networks for MR image synthesis. Medical Image Analysis 70 (2021), 101944.","journal-title":"Medical Image Analysis"},{"key":"e_1_3_1_121_2","first-page":"2528","article-title":"Deconvolutional networks","author":"Zeiler Matthew D.","year":"2010","unstructured":"Matthew D. Zeiler, Dilip Krishnan, Graham W. Taylor, and Rob Fergus. 2010. Deconvolutional networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2528\u20132535.","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_1_122_2","article-title":"Hybrid generative adversarial networks for deep MR to CT synthesis using unpaired data","author":"Zeng Guodong","year":"2019","unstructured":"Guodong Zeng and Guoyan Zheng. 2019. Hybrid generative adversarial networks for deep MR to CT synthesis using unpaired data. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_123_2","doi-asserted-by":"crossref","first-page":"106676","DOI":"10.1016\/j.cmpb.2022.106676","article-title":"BPGAN: Brain PET synthesis from MRI using generative adversarial network for multi-modal Alzheimer\u2019s disease diagnosis","volume":"217","author":"Zhang Jin","year":"2022","unstructured":"Jin Zhang, Xiaohai He, Linbo Qing, Feng Gao, and Bin Wang. 2022. BPGAN: Brain PET synthesis from MRI using generative adversarial network for multi-modal Alzheimer\u2019s disease diagnosis. Computer Methods and Programs in Biomedicine 217 (2022), 106676.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"e_1_3_1_124_2","article-title":"18F-florbetapir PET\/MRI for quantitatively monitoring myelin loss and recovery in patients with multiple sclerosis: A longitudinal study","volume":"37","author":"Zhang Min","year":"2021","unstructured":"Min Zhang, You Ni, Qinming Zhou, Lu He, Huanyu Meng, Yining Gao, Xinyun Huang, Hongping Meng, Peihan Li, Mei lian Chen, Danni Wang, J. Hu, Qiu Huang, Yao Li, Fabien Chauveau, Biao Li, and Sheng Chen. 2021. 18F-florbetapir PET\/MRI for quantitatively monitoring myelin loss and recovery in patients with multiple sclerosis: A longitudinal study. EClinicalMedicine 37 (2021).","journal-title":"EClinicalMedicine"},{"issue":"10","key":"e_1_3_1_125_2","doi-asserted-by":"crossref","first-page":"4514","DOI":"10.1109\/TNNLS.2020.3018790","article-title":"Inductive structure consistent hashing via flexible semantic calibration","volume":"32","author":"Zhang Zheng","year":"2020","unstructured":"Zheng Zhang, Luyao Liu, Yadan Luo, Zi Huang, Fumin Shen, Heng Tao Shen, and Guangming Lu. 2020. Inductive structure consistent hashing via flexible semantic calibration. IEEE Transactions on Neural Networks and Learning Systems 32, 10 (2020), 4514\u20134528.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"7","key":"e_1_3_1_126_2","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1109\/TPAMI.2018.2847335","article-title":"Binary multi-view clustering","volume":"41","author":"Zhang Zheng","year":"2018","unstructured":"Zheng Zhang, Li Liu, Fumin Shen, Heng Tao Shen, and Ling Shao. 2018. Binary multi-view clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 41, 7 (2018), 1774\u20131782.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"5","key":"e_1_3_1_127_2","first-page":"5091","article-title":"Modality-invariant asymmetric networks for cross-modal hashing","volume":"35","author":"Zhang Zheng","year":"2022","unstructured":"Zheng Zhang, Haoyang Luo, Lei Zhu, Guangming Lu, and Heng Tao Shen. 2022. Modality-invariant asymmetric networks for cross-modal hashing. IEEE Transactions on Knowledge and Data Engineering 35, 5 (2022), 5091\u20135104.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_128_2","first-page":"9242","article-title":"Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network","author":"Zhang Zizhao","year":"2018","unstructured":"Zizhao Zhang, L. Yang, and Yefeng Zheng. 2018. Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 9242\u20139251.","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_1_129_2","article-title":"Deep learning of brain magnetic resonance images: A brief review.","author":"Zhao Xingzhong","year":"2020","unstructured":"Xingzhong Zhao and Xing-Ming Zhao. 2020. Deep learning of brain magnetic resonance images: A brief review. Methods 192 (2020), 131\u2013140.","journal-title":"Methods"},{"key":"e_1_3_1_130_2","article-title":"Anatomy-constrained contrastive learning for synthetic segmentation without ground-truth","author":"Zhou Bo","year":"2021","unstructured":"Bo Zhou, Chi Liu, and James S. Duncan. 2021. Anatomy-constrained contrastive learning for synthetic segmentation without ground-truth. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_131_2","article-title":"Synthesizing multi-tracer PET images for Alzheimer\u2019s disease patients using a 3D unified anatomy-aware cyclic adversarial network","author":"Zhou Bo","year":"2021","unstructured":"Bo Zhou, Rui Wang, Ming kai Chen, Adam P. Mecca, Ryan S. O\u2019Dell, Christopher H. van Dyck, Richard E. Carson, James S. Duncan, and Chi Liu. 2021. Synthesizing multi-tracer PET images for Alzheimer\u2019s disease patients using a 3D unified anatomy-aware cyclic adversarial network. In International Conference on Medical Image Computing and Computer-assisted Intervention.","journal-title":"International Conference on Medical Image Computing and Computer-assisted Intervention"},{"key":"e_1_3_1_132_2","doi-asserted-by":"crossref","first-page":"2772","DOI":"10.1109\/TMI.2020.2975344","article-title":"Hi-net: Hybrid-fusion network for multi-modal MR image synthesis","volume":"39","author":"Zhou Tao","year":"2020","unstructured":"Tao Zhou, H. Fu, Geng Chen, Jianbing Shen, and Ling Shao. 2020. Hi-net: Hybrid-fusion network for multi-modal MR image synthesis. IEEE Transactions on Medical Imaging 39 (2020), 2772\u20132781.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_133_2","first-page":"2242","article-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","author":"Zhu Jun-Yan","year":"2017","unstructured":"Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2242\u20132251.","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision"},{"key":"e_1_3_1_134_2","doi-asserted-by":"crossref","first-page":"3438","DOI":"10.1109\/JBHI.2021.3083752","article-title":"DMC-Fusion: Deep multi-cascade fusion with classifier-based feature synthesis for medical multi-modal images","volume":"25","author":"Zuo Qing","year":"2021","unstructured":"Qing Zuo, Jianping Zhang, and Yin Yang. 2021. DMC-Fusion: Deep multi-cascade fusion with classifier-based feature synthesis for medical multi-modal images. IEEE Journal of Biomedical and Health Informatics 25 (2021), 3438\u20133449.","journal-title":"IEEE Journal of Biomedical and Health Informatics"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3625227","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3625227","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:50:03Z","timestamp":1750287003000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3625227"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":133,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3,31]]}},"alternative-id":["10.1145\/3625227"],"URL":"https:\/\/doi.org\/10.1145\/3625227","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2022-12-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-18","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}