{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:41:27Z","timestamp":1759358487103,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030597276"},{"type":"electronic","value":"9783030597283"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59728-3_33","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T10:03:00Z","timestamp":1601632980000},"page":"333-342","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Spatial Semantic-Preserving Latent Space Learning for Accelerated DWI Diagnostic Report Generation"],"prefix":"10.1007","author":[{"given":"Aydan","family":"Gasimova","sequence":"first","affiliation":[]},{"given":"Gavin","family":"Seegoolam","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Paul","family":"Bentley","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Chen, L., Bentley, P., Rueckert, D.: Fully automatic acute ischemic lesion segmentation in dwi using convolutional neural networks. NeuroImage: Clinical 15, 633\u2013643 (2017)","DOI":"10.1016\/j.nicl.2017.06.016"},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Ciritsis, A., Rossi, C., Marcon, M., Van, V.D.P., Boss, A.: Accelerated diffusion-weighted imaging for lymph node assessment in the pelvis applying simultaneous multislice acquisition: a healthy volunteer study. Medicine 97(32), e11745 (2018)","DOI":"10.1097\/MD.0000000000011745"},{"key":"33_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/978-3-030-33850-3_10","volume-title":"Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support","author":"A Gasimova","year":"2019","unstructured":"Gasimova, A.: Automated enriched medical concept generation for chest X-ray images. In: Suzuki, K., et al. (eds.) ML-CDS\/IMIMIC -2019. LNCS, vol. 11797, pp. 83\u201392. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33850-3_10"},{"issue":"6","key":"33_CR4","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1002\/mrm.10171","volume":"47","author":"MA Griswold","year":"2002","unstructured":"Griswold, M.A., et al.: Generalized autocalibrating partially parallel acquisitions (grappa). Magnetic Resonance Med. Official J. Int. Soc. Magnetic Resonance Med. 47(6), 1202\u20131210 (2002)","journal-title":"Magnetic Resonance Med. Official J. Int. Soc. Magnetic Resonance Med."},{"issue":"6","key":"33_CR5","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1002\/mrm.26977","volume":"79","author":"K Hammernik","year":"2018","unstructured":"Hammernik, K., et al.: Learning a variational network for reconstruction of accelerated MRI data. Magnetic Resonance Med. 79(6), 3055\u20133071 (2018)","journal-title":"Magnetic Resonance Med."},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Jing, B., Xie, P., Xing, E.: On the automatic generation of medical imaging reports. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2577\u20132586 (2018)","DOI":"10.18653\/v1\/P18-1240"},{"key":"33_CR8","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Lin, C.Y., Hovy, E.: Automatic evaluation of summaries using n-gram co-occurrence statistics. In: Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, pp. 150\u2013157 (2003)","DOI":"10.3115\/1073445.1073465"},{"key":"33_CR10","unstructured":"Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., Yuille, A.: Deep captioning with multimodal recurrent neural networks (M-RNN). In: ICLR (2015)"},{"issue":"7","key":"33_CR11","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1097\/RLI.0000000000000357","volume":"52","author":"A Merrem","year":"2017","unstructured":"Merrem, A., et al.: Rapid diffusion-weighted magnetic resonance imaging of the brain without susceptibility artifacts: single-shot steam with radial undersampling and iterative reconstruction. Investigative Radiol. 52(7), 428\u2013433 (2017)","journal-title":"Investigative Radiol."},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. pp. 311\u2013318. Association for Computational Linguistics (2002)","DOI":"10.3115\/1073083.1073135"},{"issue":"5","key":"33_CR13","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1002\/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S","volume":"42","author":"KP Pruessmann","year":"1999","unstructured":"Pruessmann, K.P., Weiger, M., Scheidegger, M.B., Boesiger, P.: Sense: sensitivity encoding for fast MRI. Magnetic Resonance Med. Official J. Int. Soc. Magnetic Resonance Med. 42(5), 952\u2013962 (1999)","journal-title":"Magnetic Resonance Med. Official J. Int. Soc. Magnetic Resonance Med."},{"issue":"1","key":"33_CR14","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1109\/TMI.2018.2863670","volume":"38","author":"C Qin","year":"2018","unstructured":"Qin, C., Schlemper, J., Caballero, J., Price, A.N., Hajnal, J.V., Rueckert, D.: Convolutional recurrent neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imag. 38(1), 280\u2013290 (2018)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"2","key":"33_CR15","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/TMI.2017.2760978","volume":"37","author":"J Schlemper","year":"2017","unstructured":"Schlemper, J., Caballero, J., Hajnal, J.V., Price, A.N., Rueckert, D.: A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imag. 37(2), 491\u2013503 (2017)","journal-title":"IEEE Trans. Med. Imag."},{"key":"33_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/978-3-030-00928-1_30","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"J Schlemper","year":"2018","unstructured":"Schlemper, J., et al.: Cardiac MR segmentation from undersampled k-space using deep latent representation learning. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 259\u2013267. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_30"},{"key":"33_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1007\/978-3-030-32251-9_77","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"G Seegoolam","year":"2019","unstructured":"Seegoolam, G., Schlemper, J., Qin, C., Price, A., Hajnal, J., Rueckert, D.: Exploiting motion for deep learning reconstruction of extremely-undersampled dynamic MRI. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 704\u2013712. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_77"},{"key":"33_CR18","doi-asserted-by":"crossref","unstructured":"Shin, H.C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J., Summers, R.M.: Learning to read chest x-rays: Recurrent neural cascade model for automated image annotation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2497\u20132506 (2016)","DOI":"10.1109\/CVPR.2016.274"},{"key":"33_CR19","doi-asserted-by":"crossref","unstructured":"Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3156\u20133164. IEEE (2015)","DOI":"10.1109\/CVPR.2015.7298935"},{"issue":"4","key":"33_CR20","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"issue":"5","key":"33_CR21","doi-asserted-by":"publisher","first-page":"1507","DOI":"10.1002\/jmri.25665","volume":"46","author":"J Weiss","year":"2017","unstructured":"Weiss, J., et al.: Feasibility of accelerated simultaneous multislice diffusion-weighted MRI of the prostate. J. Magnetic Resonance Imag. 46(5), 1507\u20131515 (2017)","journal-title":"J. Magnetic Resonance Imag."},{"issue":"3","key":"33_CR22","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1002\/jmri.25664","volume":"46","author":"W Wu","year":"2017","unstructured":"Wu, W., Miller, K.L.: Image formation in diffusion MRI: a review of recent technical developments. J. Magnetic Resonance Imag. 46(3), 646\u2013662 (2017)","journal-title":"J. Magnetic Resonance Imag."},{"key":"33_CR23","unstructured":"Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: International Conference on Machine Learning, pp. 2048\u20132057 (2015)"},{"key":"33_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/978-3-030-00928-1_52","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Y Xue","year":"2018","unstructured":"Xue, Y., et al.: Multimodal recurrent model with attention for automated radiology report generation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 457\u2013466. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_52"},{"key":"33_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/978-3-030-32226-7_80","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Yuan","year":"2019","unstructured":"Yuan, J., Liao, H., Luo, R., Luo, J.: Automatic radiology report generation based on multi-view image fusion and medical concept enrichment. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 721\u2013729. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_80"},{"key":"33_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Xie, Y., Xing, F., McGough, M., Yang, L.: Mdnet: a semantically and visually interpretable medical image diagnosis network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6428\u20136436 (2017)","DOI":"10.1109\/CVPR.2017.378"},{"issue":"7697","key":"33_CR27","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1038\/nature25988","volume":"555","author":"B Zhu","year":"2018","unstructured":"Zhu, B., Liu, J.Z., Cauley, S.F., Rosen, B.R., Rosen, M.S.: Image reconstruction by domain-transform manifold learning. Nature 555(7697), 487\u2013492 (2018)","journal-title":"Nature"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59728-3_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:06:27Z","timestamp":1759356387000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59728-3_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597276","9783030597283"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59728-3_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.org\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1809","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"542","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}