{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:40:42Z","timestamp":1759358442380,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030598600"},{"type":"electronic","value":"9783030598617"}],"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\/978-3-030-59861-7_25","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T03:07:37Z","timestamp":1601608057000},"page":"241-249","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint Appearance-Feature Domain Adaptation: Application to QSM Segmentation Transfer"],"prefix":"10.1007","author":[{"given":"Bin","family":"Xiao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naying","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuhua","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"issue":"3","key":"25_CR1","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.1016\/j.neuroimage.2012.05.049","volume":"62","author":"C Langkammer","year":"2012","unstructured":"Langkammer, C., Schweser, F., Krebs, N., et al.: Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. Neuroimage 62(3), 1593\u20131599 (2012)","journal-title":"Neuroimage"},{"doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","key":"25_CR2","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"2","key":"25_CR3","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Tsang, I.W., Kwok, J.T., et al.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199\u2013210 (2010)","journal-title":"IEEE Trans. Neural Netw."},{"key":"25_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1007\/978-3-319-13560-1_76","volume-title":"PRICAI 2014: Trends in Artificial Intelligence","author":"Muhammad Ghifary","year":"2014","unstructured":"Ghifary, Muhammad, Kleijn, W.B., Zhang, Mengjie: Domain adaptive neural networks for object recognition. In: Pham, Duc-Nghia, Park, Seong-Bae (eds.) PRICAI 2014. LNCS (LNAI), vol. 8862, pp. 898\u2013904. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-13560-1_76"},{"unstructured":"Tzeng, E., Hoffman, J., Zhang, N., et al.: Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014)","key":"25_CR5"},{"unstructured":"Long, M., Cao, Y., Wang, J., et al.: Learning transferable features with deep adaptation networks. arXiv preprint arXiv:1502.02791 (2015)","key":"25_CR6"},{"unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)","key":"25_CR7"},{"key":"25_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-030-01240-3_12","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Minjun Li","year":"2018","unstructured":"Li, Minjun, Huang, Haozhi, Ma, Lin, Liu, Wei, Zhang, Tong, Jiang, Yugang: Unsupervised image-to-image translation with stacked cycle-consistent adversarial networks. In: Ferrari, Vittorio, Hebert, Martial, Sminchisescu, Cristian, Weiss, Yair (eds.) ECCV 2018. LNCS, vol. 11213, pp. 186\u2013201. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01240-3_12"},{"doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223\u20132232 (2017)","key":"25_CR9","DOI":"10.1109\/ICCV.2017.244"},{"doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","key":"25_CR10","DOI":"10.1109\/CVPR.2018.00745"},{"unstructured":"Liu, H., Simonyan, K., Yang, Y.: Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055 (2018)","key":"25_CR11"},{"key":"25_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1007\/978-3-030-32254-0_63","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Daniel Bug","year":"2019","unstructured":"Bug, Daniel, et al.: Combined learning for similar tasks with domain-switching networks. In: Shen, Dinggang, Liu, Tianming, Peters, Terry M., Staib, Lawrence H., Essert, Caroline, Zhou, Sean, Yap, Pew-Thian, Khan, Ali (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 565\u2013572. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32254-0_63"},{"doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., et al.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","key":"25_CR13","DOI":"10.1109\/CVPR.2017.243"},{"doi-asserted-by":"crossref","unstructured":"J\u00e9gou, S., Drozdzal, M., Vazquez, D., et al.: The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 11\u201319 (2017)","key":"25_CR14","DOI":"10.1109\/CVPRW.2017.156"},{"key":"25_CR15","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1016\/j.neuroimage.2010.11.088","volume":"55","author":"W Li","year":"2011","unstructured":"Li, W., Wu, B., Liu, C.: Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition. Neuroimage 55, 1645\u20131656 (2011)","journal-title":"Neuroimage"},{"issue":"5","key":"25_CR16","doi-asserted-by":"publisher","first-page":"1396","DOI":"10.1002\/mrm.24384","volume":"69","author":"J Tang","year":"2013","unstructured":"Tang, J., Liu, S., Neelavalli, J., et al.: Improving susceptibility mapping using a threshold-based K-space\/image domain iterative reconstruction approach. Magnet. Resonance Med. 69(5), 1396\u20131407 (2013)","journal-title":"Magnet. Resonance Med."},{"issue":"365","key":"25_CR17","first-page":"1","volume":"2","author":"BB Avants","year":"2009","unstructured":"Avants, B.B., Tustison, N., Song, G.: Advanced normalization tools (ANTS). Insight J 2(365), 1\u201335 (2009)","journal-title":"Insight J"},{"doi-asserted-by":"crossref","unstructured":"Xiao, B., He, N., Wang, Q., et al.: Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson\u2019s disease. NeuroImage: Clin. 24, 102070 (2019)","key":"25_CR18","DOI":"10.1016\/j.nicl.2019.102070"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59861-7_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:04:34Z","timestamp":1759356274000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59861-7_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030598600","9783030598617"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59861-7_25","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":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","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":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mlmi2020.web.unc.edu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"101","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":"68","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":"67% - 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":"2.04","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":"3.43","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)"}}]}}