{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T16:38:38Z","timestamp":1768408718228,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":57,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972188, 62122035"],"award-info":[{"award-number":["61972188, 62122035"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3503161.3547762","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T15:43:01Z","timestamp":1665416581000},"page":"1522-1531","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["FedMed-ATL: Misaligned Unpaired Cross-Modality Neuroimage Synthesis via Affine Transform Loss"],"prefix":"10.1145","author":[{"given":"Jinbao","family":"Wang","sequence":"first","affiliation":[{"name":"Southern University of Science and Technology, Shenzhen, China"}]},{"given":"Guoyang","family":"Xie","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology & University of Surrey, Shenzhen, China"}]},{"given":"Yawen","family":"Huang","sequence":"additional","affiliation":[{"name":"Tencent Jarvis Lab, Shenzhen, China"}]},{"given":"Yefeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Tencent Jarvis Lab, Shenzhen, China"}]},{"given":"Yaochu","family":"Jin","sequence":"additional","affiliation":[{"name":"Bielefeld University & University of Surrey, Bielefeld, Germany"}]},{"given":"Feng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2011.2162529"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01342"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.2000.0582"},{"key":"e_1_3_2_2_5_1","unstructured":"Sean Augenstein H Brendan McMahan Daniel Ramage Swaroop Ramaswamy Peter Kairouz Mingqing Chen Rajiv Mathews etal 2019. Generative models for effective ML on private decentralized datasets. arXiv preprint arXiv:1911.06679 (2019). Sean Augenstein H Brendan McMahan Daniel Ramage Swaroop Ramaswamy Peter Kairouz Mingqing Chen Rajiv Mathews et al. 2019. Generative models for effective ML on private decentralized datasets. arXiv preprint arXiv:1911.06679 (2019)."},{"key":"e_1_3_2_2_6_1","unstructured":"Sean Augenstein H. Brendan McMahan Daniel Ramage Swaroop Ramaswamy Peter Kairouz Mingqing Chen Rajiv Mathews and Blaise Ag\u00fcera y Arcas. 2020. Generative Models for Effective ML on Private Decentralized Datasets. In ICLR. OpenReview.net. Sean Augenstein H. Brendan McMahan Daniel Ramage Swaroop Ramaswamy Peter Kairouz Mingqing Chen Rajiv Mathews and Blaise Ag\u00fcera y Arcas. 2020. Generative Models for Effective ML on Private Decentralized Datasets. In ICLR. OpenReview.net."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.09.025"},{"key":"e_1_3_2_2_8_1","volume-title":"Multiresolution elastic matching. Computer vision, graphics, and image processing 46, 1","author":"Bajcsy Ruzena","year":"1989","unstructured":"Ruzena Bajcsy and Stane Kova?i?. 1989. Multiresolution elastic matching. Computer vision, graphics, and image processing 46, 1 ( 1989 ), 1--21. Ruzena Bajcsy and Stane Kova?i?. 1989. Multiresolution elastic matching. Computer vision, graphics, and image processing 46, 1 (1989), 1--21."},{"key":"e_1_3_2_2_9_1","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. In Lecture Notes in Computer Science.","author":"Bakas Spyridon","year":"2017","unstructured":"Spyridon Bakas , Hugo J. Kuijf , Bjoern H. Menze , and Mauricio Reyes . 2017 . Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. In Lecture Notes in Computer Science. Spyridon Bakas, Hugo J. Kuijf, Bjoern H. Menze, and Mauricio Reyes. 2017. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. In Lecture Notes in Computer Science."},{"key":"e_1_3_2_2_10_1","volume-title":"Voxelmorph: a learning framework for deformable medical image registration","author":"Balakrishnan Guha","year":"2019","unstructured":"Guha Balakrishnan , Amy Zhao , Mert R Sabuncu , John Guttag , and Adrian V Dalca . 2019. Voxelmorph: a learning framework for deformable medical image registration . IEEE transactions on medical imaging 38, 8 ( 2019 ), 1788--1800. Guha Balakrishnan, Amy Zhao, Mert R Sabuncu, John Guttag, and Adrian V Dalca. 2019. Voxelmorph: a learning framework for deformable medical image registration. IEEE transactions on medical imaging 38, 8 (2019), 1788--1800."},{"key":"e_1_3_2_2_11_1","volume-title":"Gs-wgan: A gradient-sanitized approach for learning differentially private generators. arXiv preprint arXiv:2006.08265","author":"Chen Dingfan","year":"2020","unstructured":"Dingfan Chen , Tribhuvanesh Orekondy , and Mario Fritz . 2020 . Gs-wgan: A gradient-sanitized approach for learning differentially private generators. arXiv preprint arXiv:2006.08265 (2020). Dingfan Chen, Tribhuvanesh Orekondy, and Mario Fritz. 2020. Gs-wgan: A gradient-sanitized approach for learning differentially private generators. arXiv preprint arXiv:2006.08265 (2020)."},{"key":"e_1_3_2_2_12_1","unstructured":"Dingfan Chen Tribhuvanesh Orekondy and Mario Fritz. 2020. GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators. In NeurIPS. Dingfan Chen Tribhuvanesh Orekondy and Mario Fritz. 2020. GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators. In NeurIPS."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00819"},{"key":"e_1_3_2_2_14_1","volume-title":"Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces. Medical image analysis 57","author":"Dalca Adrian V.","year":"2019","unstructured":"Adrian V. Dalca , Guha Balakrishnan , John V. Guttag , and Mert Rory Sabuncu . 2019. Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces. Medical image analysis 57 ( 2019 ), 226--236. Adrian V. Dalca, Guha Balakrishnan, John V. Guttag, and Mert Rory Sabuncu. 2019. Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces. Medical image analysis 57 (2019), 226--236."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47118-1_8"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2901750"},{"key":"e_1_3_2_2_17_1","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention 11070","author":"Fan Jingfan","year":"2018","unstructured":"Jingfan Fan , Xiaohuan Cao , Zhong Xue , Pew-Thian Yap , and Dinggang Shen . 2018 . Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning Based Registration. Medical image computing and computer-assisted intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 11070 (2018), 739--746. Jingfan Fan, Xiaohuan Cao, Zhong Xue, Pew-Thian Yap, and Dinggang Shen. 2018. Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning Based Registration. Medical image computing and computer-assisted intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 11070 (2018), 739--746."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3046460"},{"key":"e_1_3_2_2_19_1","volume-title":"Heinrich and Lasse Hansen","author":"Mattias","year":"2020","unstructured":"Mattias P. Heinrich and Lasse Hansen . 2020 . Highly Accurate and Memory Efficient Unsupervised Learning-Based Discrete CT Registration Using 2.5D Displacement Search. In MICCAI. Mattias P. Heinrich and Lasse Hansen. 2020. Highly Accurate and Memory Efficient Unsupervised Learning-Based Discrete CT Registration Using 2.5D Displacement Search. In MICCAI."},{"key":"e_1_3_2_2_20_1","volume-title":"ECCV (3) (Lecture Notes in Computer Science","author":"Huang Xun","unstructured":"Xun Huang , Ming-Yu Liu , Serge J. Belongie , and Jan Kautz . 2018. Multimodal Unsupervised Image-to-Image Translation . In ECCV (3) (Lecture Notes in Computer Science , Vol. 11207). Springer, 179-- 196 . Xun Huang, Ming-Yu Liu, Serge J. Belongie, and Jan Kautz. 2018. Multimodal Unsupervised Image-to-Image Translation. In ECCV (3) (Lecture Notes in Computer Science, Vol. 11207). Springer, 179--196."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3011557"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","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 IJCAI. 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 IJCAI.","DOI":"10.24963\/ijcai.2020\/90"},{"key":"e_1_3_2_2_23_1","volume-title":"Efros","author":"Isola Phillip","year":"2017","unstructured":"Phillip Isola , Jun-Yan Zhu , Tinghui Zhou , and Alexei A . Efros . 2017 . Imageto- Image Translation with Conditional Adversarial Networks. In CVPR. IEEE Computer Society , 5967--5976. Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. 2017. Imageto- Image Translation with Conditional Adversarial Networks. In CVPR. IEEE Computer Society, 5967--5976."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3108881"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32226-7_89"},{"key":"e_1_3_2_2_26_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2008.12.037"},{"key":"e_1_3_2_2_28_1","unstructured":"Lingke Kong Chenyu Lian Detian Huang Zhenjiang Li Yanle Hu and Qichao Zhou. 2021. Breaking the Dilemma of Medical Image-to-image Translation. arXiv:2110.06465 [eess.IV] Lingke Kong Chenyu Lian Detian Huang Zhenjiang Li Yanle Hu and Qichao Zhou. 2021. Breaking the Dilemma of Medical Image-to-image Translation. arXiv:2110.06465 [eess.IV]"},{"key":"e_1_3_2_2_29_1","volume-title":"Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith.","author":"Li Tian","year":"2020","unstructured":"Tian Li , Anit Kumar Sahu , Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020 . Federated Optimization in Heterogeneous Networks. In MLSys . mlsys.org. Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated Optimization in Heterogeneous Networks. In MLSys. mlsys.org."},{"key":"e_1_3_2_2_30_1","unstructured":"Ming-Yu Liu Thomas M. Breuel and Jan Kautz. 2017. Unsupervised Image-to- Image Translation Networks. In NIPS. 700--708. Ming-Yu Liu Thomas M. Breuel and Jan Kautz. 2017. Unsupervised Image-to- Image Translation Networks. In NIPS. 700--708."},{"key":"e_1_3_2_2_31_1","unstructured":"Risheng Liu Zi Li Yuxi Zhang Xin Fan and Zhongxuan Luo. 2020. Bi-level Probabilistic Feature Learning for Deformable Image Registration. In IJCAI. Risheng Liu Zi Li Yuxi Zhang Xin Fan and Zhongxuan Luo. 2020. Bi-level Probabilistic Feature Learning for Deformable Image Registration. In IJCAI."},{"key":"e_1_3_2_2_32_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR 1273--1282. Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR 1273--1282."},{"key":"e_1_3_2_2_33_1","volume-title":"AISTATS (Proceedings of Machine Learning Research","volume":"1282","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan , Eider Moore , Daniel Ramage , Seth Hampson , and Blaise Ag\u00fcera y Arcas . 2017 . Communication-Efficient Learning of Deep Networks from Decentralized Data . In AISTATS (Proceedings of Machine Learning Research , Vol. 54). PMLR, 1273-- 1282 . Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag\u00fcera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In AISTATS (Proceedings of Machine Learning Research, Vol. 54). PMLR, 1273--1282."},{"key":"e_1_3_2_2_34_1","volume-title":"Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Tony","year":"2020","unstructured":"Tony C.W. Mok and Albert C. S. Chung. 2020 . Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ( 2020 ), 4643--4652. Tony C.W. Mok and Albert C. S. Chung. 2020. Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020), 4643--4652."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/10704282_64"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3059726"},{"key":"e_1_3_2_2_37_1","unstructured":"Marc-Michel Roh\u00e9 Manasi Datar Tobias Heimann Maxime Sermesant and Xavier Pennec. 2017. SVF-Net: Learning Deformable Image Registration Using Shape Matching. In MICCAI. Marc-Michel Roh\u00e9 Manasi Datar Tobias Heimann Maxime Sermesant and Xavier Pennec. 2017. SVF-Net: Learning Deformable Image Registration Using Shape Matching. In MICCAI."},{"key":"e_1_3_2_2_38_1","volume-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv abs\/1505.04597","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger , Philipp Fischer , and Thomas Brox . 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv abs\/1505.04597 ( 2015 ). Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv abs\/1505.04597 (2015)."},{"key":"e_1_3_2_2_39_1","volume-title":"Martin O Leach, and David J Hawkes.","author":"Rueckert Daniel","year":"1999","unstructured":"Daniel Rueckert , Luke I Sonoda , Carmel Hayes , Derek LG 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--721. Daniel Rueckert, Luke I Sonoda, Carmel Hayes, Derek LG 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--721."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2945521"},{"key":"e_1_3_2_2_41_1","volume-title":"HAMMER: hierarchical attribute matching mechanism for elastic registration","author":"Shen Dinggang","year":"2002","unstructured":"Dinggang Shen and Christos Davatzikos . 2002. HAMMER: hierarchical attribute matching mechanism for elastic registration . IEEE transactions on medical imaging 21, 11 ( 2002 ), 1421--1439. Dinggang Shen and Christos Davatzikos. 2002. HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE transactions on medical imaging 21, 11 (2002), 1421--1439."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3046444"},{"key":"e_1_3_2_2_43_1","volume-title":"Cancer statistics","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). Rebecca L. Siegel, Kimberly D Miller, and Ahmedin Jemal. 2019. Cancer statistics, 2019. CA: A Cancer Journal for Clinicians 69 (2019)."},{"key":"e_1_3_2_2_44_1","volume-title":"Federated CycleGAN for Privacy- Preserving Image-to-Image Translation. CoRR abs\/2106.09246","author":"Song Joonyoung","year":"2021","unstructured":"Joonyoung Song and Jong Chul Ye. 2021. Federated CycleGAN for Privacy- Preserving Image-to-Image Translation. CoRR abs\/2106.09246 ( 2021 ). Joonyoung Song and Jong Chul Ye. 2021. Federated CycleGAN for Privacy- Preserving Image-to-Image Translation. CoRR abs\/2106.09246 (2021)."},{"key":"e_1_3_2_2_45_1","volume-title":"Image matching as a diffusion process: an analogy with Maxwell's demons. Medical image analysis 2, 3","author":"Thirion J-P","year":"1998","unstructured":"J-P Thirion . 1998. Image matching as a diffusion process: an analogy with Maxwell's demons. Medical image analysis 2, 3 ( 1998 ), 243--260. J-P Thirion. 1998. Image matching as a diffusion process: an analogy with Maxwell's demons. Medical image analysis 2, 3 (1998), 243--260."},{"key":"e_1_3_2_2_46_1","volume-title":"Boyang Liu, Karina V Bunting, Victoria M. Stoll, Antonio de Marvao, Declan P. O'Regan, Georgios V. Gkoutos, Dipak Kotecha, and Jinming Duan.","author":"Thorley Alexander","year":"2021","unstructured":"Alexander Thorley , Xi Jia , Hyung Jin Chang , Boyang Liu, Karina V Bunting, Victoria M. Stoll, Antonio de Marvao, Declan P. O'Regan, Georgios V. Gkoutos, Dipak Kotecha, and Jinming Duan. 2021 . Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration. In MICCAI. Alexander Thorley, Xi Jia, Hyung Jin Chang, Boyang Liu, Karina V Bunting, Victoria M. Stoll, Antonio de Marvao, Declan P. O'Regan, Georgios V. Gkoutos, Dipak Kotecha, and Jinming Duan. 2021. Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration. In MICCAI."},{"key":"e_1_3_2_2_47_1","unstructured":"Hongyi Wang Mikhail Yurochkin Yuekai Sun Dimitris S. Papailiopoulos and Yasaman Khazaeni. 2020. Federated Learning with Matched Averaging. In ICLR. OpenReview.net. Hongyi Wang Mikhail Yurochkin Yuekai Sun Dimitris S. Papailiopoulos and Yasaman Khazaeni. 2020. Federated Learning with Matched Averaging. In ICLR. OpenReview.net."},{"key":"e_1_3_2_2_48_1","volume-title":"DeepFLASH: An Efficient Network for Learning-Based Medical Image Registration. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Wang Jian","year":"2020","unstructured":"Jian Wang and Miaomiao Zhang . 2020 . DeepFLASH: An Efficient Network for Learning-Based Medical Image Registration. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020), 4443--4451. Jian Wang and Miaomiao Zhang. 2020. DeepFLASH: An Efficient Network for Learning-Based Medical Image Registration. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020), 4443--4451."},{"key":"e_1_3_2_2_49_1","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention 11070","author":"Zhou Luping","year":"2018","unstructured":"YanWang, Luping Zhou , LeiWang, 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. Medical image computing and computer-assisted intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 11070 (2018), 329--337. YanWang, Luping Zhou, LeiWang, 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. Medical image computing and computer-assisted intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 11070 (2018), 329--337."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"crossref","unstructured":"Guoyang Xie Jinbao Wang Yawen Huang Yuexiang Li Yefeng Zheng Feng Zheng and Yaochu Jin. 2022. FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis. Guoyang Xie Jinbao Wang Yawen Huang Yuexiang Li Yefeng Zheng Feng Zheng and Yaochu Jin. 2022. FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis.","DOI":"10.1016\/j.neucom.2023.126282"},{"key":"e_1_3_2_2_51_1","volume-title":"LDVoxelMorph: A precise loss function and cascaded architecture for unsupervised diffeomorphic large displacement registration. Medical physics","author":"Yang Jing","year":"2022","unstructured":"Jing Yang , Yinghao Wu , Dong Zhang , Wenting Cui , Xiaoli Yue , S. Du , and Hongmei Zhang . 2022. LDVoxelMorph: A precise loss function and cascaded architecture for unsupervised diffeomorphic large displacement registration. Medical physics ( 2022 ). Jing Yang, Yinghao Wu, Dong Zhang, Wenting Cui, Xiaoli Yue, S. Du, and Hongmei Zhang. 2022. LDVoxelMorph: A precise loss function and cascaded architecture for unsupervised diffeomorphic large displacement registration. Medical physics (2022)."},{"key":"e_1_3_2_2_52_1","volume-title":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","author":"Yu Biting","year":"2018","unstructured":"Biting Yu , Luping Zhou , Lei Wang , Jurgen Fripp , and Pierrick T. Bourgeat . 2018. 3D cGAN based cross-modalityMRimage synthesis for brain tumor segmentation . 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) ( 2018 ), 626--630. Biting Yu, Luping Zhou, Lei Wang, Jurgen Fripp, and Pierrick T. Bourgeat. 2018. 3D cGAN based cross-modalityMRimage synthesis for brain tumor segmentation. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (2018), 626--630."},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2969630"},{"key":"e_1_3_2_2_54_1","volume-title":"ICML (Proceedings of Machine Learning Research","volume":"7261","author":"Yurochkin Mikhail","year":"2019","unstructured":"Mikhail Yurochkin , Mayank Agarwal , Soumya Ghosh , Kristjan H. Greenewald , Trong Nghia Hoang , and Yasaman Khazaeni . 2019 . Bayesian Nonparametric Federated Learning of Neural Networks . In ICML (Proceedings of Machine Learning Research , Vol. 97). PMLR, 7252-- 7261 . Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni. 2019. Bayesian Nonparametric Federated Learning of Neural Networks. In ICML (Proceedings of Machine Learning Research, Vol. 97). PMLR, 7252--7261."},{"key":"e_1_3_2_2_55_1","volume-title":"Duncan","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 MICCAI. Bo Zhou, Chi Liu, and James S. Duncan. 2021. Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth. In MICCAI."},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3083752"}],"event":{"name":"MM '22: The 30th ACM International Conference on Multimedia","location":"Lisboa Portugal","acronym":"MM '22","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 30th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3547762","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3547762","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:40Z","timestamp":1750188640000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3547762"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":57,"alternative-id":["10.1145\/3503161.3547762","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3547762","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2022-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}