{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T14:50:59Z","timestamp":1784040659176,"version":"3.55.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031721038","type":"print"},{"value":"9783031721045","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-72104-5_66","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:02:53Z","timestamp":1727870573000},"page":"690-700","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Structural Attention: Rethinking Transformer for\u00a0Unpaired Medical Image Synthesis"],"prefix":"10.1007","author":[{"given":"Vu Minh Hieu","family":"Phan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yutong","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuankai","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhibin","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonios","family":"Perperidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Son Lam","family":"Phung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johan W.","family":"Verjans","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minh-Son","family":"To","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"66_CR1","doi-asserted-by":"crossref","unstructured":"Learning with radiomics for disease diagnosis and treatment planning: a review. Front. Oncol. 12, 773840 (2022)","DOI":"10.3389\/fonc.2022.773840"},{"key":"66_CR2","unstructured":"Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"66_CR3","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, X., Zhou, J., Qiao, Y., Dong, C.: Activating more pixels in image super-resolution transformer. In: CVPR, pp. 22367\u201322377 (2023)","DOI":"10.1109\/CVPR52729.2023.02142"},{"key":"66_CR4","doi-asserted-by":"crossref","unstructured":"Cui, C., et al.: Deep multi-modal fusion of image and non-image data in disease diagnosis and prognosis: a review. Prog. Biomed. Engineer (2023)","DOI":"10.1088\/2516-1091\/acc2fe"},{"issue":"8","key":"66_CR5","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.3390\/diagnostics11081384","volume":"11","author":"Y Dai","year":"2021","unstructured":"Dai, Y., Gao, Y., Liu, F.: TransMed: transformers advance multi-modal medical image classification. Diagnostics 11(8), 1384 (2021)","journal-title":"Diagnostics"},{"issue":"10","key":"66_CR6","doi-asserted-by":"publisher","first-page":"2598","DOI":"10.1109\/TMI.2022.3167808","volume":"41","author":"O Dalmaz","year":"2022","unstructured":"Dalmaz, O., Yurt, M., \u00c7ukur, T.: ResViT: residual vision transformers for multimodal medical image synthesis. IEEE Trans. Med. Imag. 41(10), 2598\u20132614 (2022)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"4","key":"66_CR7","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/S1474-4422(13)70024-3","volume":"12","author":"D Doherty","year":"2013","unstructured":"Doherty, D., Millen, K.J., Barkovich, A.J.: Midbrain and hindbrain malformations: advances in clinical diagnosis, imaging, and genetics. Lancet Neurol. 12(4), 381\u2013393 (2013)","journal-title":"Lancet Neurol."},{"key":"66_CR8","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. In: Proceedings of the International Conference on Learning Representations (2021)"},{"key":"66_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1007\/978-3-030-87231-1_46","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"H Emami","year":"2021","unstructured":"Emami, H., Dong, M., Nejad-Davarani, S.P., Glide-Hurst, C.K.: SA-GAN: structure-aware GAN for organ-preserving synthetic CT generation. In: de Bruijne, M., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 471\u2013481. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87231-1_46"},{"issue":"1","key":"66_CR10","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1038\/s41597-022-01718-3","volume":"9","author":"S Gatidis","year":"2022","unstructured":"Gatidis, S., et al.: A whole-body FDG-PET\/CT dataset with manually annotated tumor lesions. Sci. Data 9(1), 601 (2022)","journal-title":"Sci. Data"},{"key":"66_CR11","doi-asserted-by":"crossref","unstructured":"Ge, Y., et al.: Unpaired MR to CT synthesis with explicit structural constrained adversarial learning. In: IEEE International Symposium on Biomedical Imaging. IEEE (2019)","DOI":"10.1109\/ISBI.2019.8759529"},{"key":"66_CR12","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: UNETR: transformers for 3D medical image segmentation. In: WACV, pp. 574\u2013584 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"66_CR13","doi-asserted-by":"crossref","unstructured":"Hu, X., Zhou, X., Huang, Q., Shi, Z., Sun, L., Li, Q.: QS-Attn: query-selected attention for contrastive learning in I2I translation. In: CVPR, pp. 18291\u201318300 (2022)","DOI":"10.1109\/CVPR52688.2022.01775"},{"key":"66_CR14","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)"},{"key":"66_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2021.101953","volume":"91","author":"Y Liu","year":"2021","unstructured":"Liu, Y., et al.: CT synthesis from MRI using multi-cycle GAN for head-and-neck radiation therapy. Computer. Med. Imag. Graphic. 91, 101953 (2021)","journal-title":"Computer. Med. Imag. Graphic."},{"key":"66_CR16","first-page":"14663","volume":"35","author":"Z Lu","year":"2022","unstructured":"Lu, Z., Xie, H., Liu, C., Zhang, Y.: Bridging the gap between vision transformers and convolutional neural networks on small datasets. Proc. Adv. Neural Inform. Process. Syst. 35, 14663\u201314677 (2022)","journal-title":"Proc. Adv. Neural Inform. Process. Syst."},{"issue":"1","key":"66_CR17","doi-asserted-by":"publisher","first-page":"11090","DOI":"10.1038\/s41598-022-14677-x","volume":"12","author":"H Matsuo","year":"2022","unstructured":"Matsuo, H., et al.: Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks. Sci. Rep. 12(1), 11090 (2022)","journal-title":"Sci. Rep."},{"issue":"1","key":"66_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13550-021-00830-6","volume":"11","author":"I M\u00e9rida","year":"2021","unstructured":"M\u00e9rida, I., et al.: CERMEP-IDB-MRXFDG: a database of 37 normal adult human brain [18F] FDG PET, T1 and FLAIR MRI, and CT images available for research. EJNMMI Res. 11(1), 1\u201310 (2021)","journal-title":"EJNMMI Res."},{"key":"66_CR19","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/978-3-031-43999-5_6","volume-title":"MICCAI 2023","author":"VMH Phan","year":"2023","unstructured":"Phan, V.M.H., Liao, Z., Verjans, J.W., To, M.S.: Structure-preserving synthesis: MaskGAN for unpaired MR-CT translation. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14229, pp. 56\u201365. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43999-5_6"},{"key":"66_CR20","first-page":"12116","volume":"34","author":"M Raghu","year":"2021","unstructured":"Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., Dosovitskiy, A.: Do vision transformers see like convolutional neural networks? Proc. Adv. Neural Inform. Process. Syst. 34, 12116\u201312128 (2021)","journal-title":"Proc. Adv. Neural Inform. Process. Syst."},{"key":"66_CR21","doi-asserted-by":"crossref","unstructured":"Richardson, D.B., et\u00a0al.: Risk of cancer from occupational exposure to ionising radiation: retrospective cohort study of workers in France, the United Kingdom, and the United States. BMJ 351 (2015)","DOI":"10.1136\/bmj.h5359"},{"key":"66_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.03.072","volume":"538","author":"NC Ristea","year":"2023","unstructured":"Ristea, N.C., et al.: CyTran: a cycle-consistent transformer with multi-level consistency for non-contrast to contrast CT translation. Neurocomputing 538, 126211 (2023)","journal-title":"Neurocomputing"},{"key":"66_CR23","doi-asserted-by":"publisher","first-page":"1972","DOI":"10.1109\/TNNLS.2021.3105725","volume":"34","author":"H Tang","year":"2021","unstructured":"Tang, H., Liu, H., Xu, D., Torr, P.H., Sebe, N.: AttentionGAN: unpaired image-to-image translation using attention-guided generative adversarial networks. IEEE Trans. Neu. Netw. Learn. Syst. 34, 1972\u20131987 (2021)","journal-title":"IEEE Trans. Neu. Netw. Learn. Syst."},{"key":"66_CR24","doi-asserted-by":"crossref","unstructured":"Tang, Y., et al.: Self-supervised pre-training of swin transformers for 3D medical image analysis. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.02007"},{"key":"66_CR25","doi-asserted-by":"crossref","unstructured":"Torbunov, D., et al.: UVCGAN: UNet vision transformer cycle-consistent GAN for unpaired image-to-image translation. In: WACV, pp. 702\u2013712 (2023)","DOI":"10.1109\/WACV56688.2023.00077"},{"key":"66_CR26","unstructured":"Vaswani, A., et al.: Attention is all you need 30 (2017)"},{"issue":"12","key":"66_CR27","doi-asserted-by":"publisher","first-page":"4249","DOI":"10.1109\/TMI.2020.3015379","volume":"39","author":"H Yang","year":"2020","unstructured":"Yang, H., et al.: Unsupervised MR-to-CT synthesis using structure-constrained CycleGAN. IEEE Trans. Med. Imag. 39(12), 4249\u20134261 (2020)","journal-title":"IEEE Trans. Med. Imag."},{"key":"66_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-3-030-00889-5_20","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"H Yang","year":"2018","unstructured":"Yang, H., et al.: Unpaired brain MR-to-CT synthesis using a structure-constrained CycleGAN. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 174\u2013182. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_20"},{"key":"66_CR29","doi-asserted-by":"crossref","unstructured":"Yu, F., Wang, X., Cao, M., Li, G., Shan, Y., Dong, C.: OSRT: omnidirectional image super-resolution with distortion-aware transformer. In: CVPR, pp. 13283\u201313292 (2023)","DOI":"10.1109\/CVPR52729.2023.01276"},{"key":"66_CR30","doi-asserted-by":"publisher","first-page":"1126","DOI":"10.1007\/s11263-023-01894-8","volume":"132","author":"B Zhang","year":"2023","unstructured":"Zhang, B., Liu, L., Phan, M.H., Tian, Z., Shen, C., Liu, Y.: SegVitv2: exploring efficient and continual semantic segmentation with plain vision transformers. Int. J. Comput. Vis. 132, 1126\u20131147 (2023)","journal-title":"Int. J. Comput. Vis."},{"key":"66_CR31","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1007\/978-3-031-16446-0_72","volume-title":"MICCAI 2022","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Cui, Z., Jiang, C., Zhang, J., Gao, F., Shen, D.: Mapping in cycles: dual-domain PET-CT synthesis framework with cycle-consistent constraints. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13436, pp. 758\u2013767. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16446-0_72"},{"issue":"10","key":"66_CR32","doi-asserted-by":"publisher","first-page":"2925","DOI":"10.1109\/TMI.2022.3174827","volume":"41","author":"X Zhang","year":"2022","unstructured":"Zhang, X., et al.: PTNet3D: a 3D high-resolution longitudinal infant brain MRI synthesizer based on transformers. IEEE Trans. Med. Imag. 41(10), 2925\u20132940 (2022)","journal-title":"IEEE Trans. Med. Imag."},{"key":"66_CR33","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72104-5_66","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T09:12:53Z","timestamp":1733562773000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72104-5_66"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721038","9783031721045"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72104-5_66","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}