{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:52:48Z","timestamp":1742928768567,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030922696"},{"type":"electronic","value":"9783030922702"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-92270-2_24","type":"book-chapter","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T11:06:00Z","timestamp":1638788760000},"page":"273-284","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Generative Adversarial Domain Generalization via\u00a0Cross-Task Feature Attention Learning for\u00a0Prostate Segmentation"],"prefix":"10.1007","author":[{"given":"Yifang","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enbei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianwei","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"24_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1007\/978-3-030-68107-4_21","volume-title":"Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges","author":"L Li","year":"2021","unstructured":"Li, L., et al.: Random style transfer based domain generalization networks integrating shape and spatial information. In: Puyol Anton, E., et al. (eds.) STACOM 2020. LNCS, vol. 12592, pp. 208\u2013218. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68107-4_21"},{"doi-asserted-by":"crossref","unstructured":"Zhang, L., Wang, X., Yang, D., Sanford, T., Harmon, S., et al.: Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation. IEEE TMI (2020)","key":"24_CR2","DOI":"10.1109\/TMI.2020.2973595"},{"doi-asserted-by":"crossref","unstructured":"Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y.Z., Hospedales, T.M.: Episodic training for domain generalization. In: ICCV, pp. 1446\u20131455 (2019)","key":"24_CR3","DOI":"10.1109\/ICCV.2019.00153"},{"doi-asserted-by":"crossref","unstructured":"Aslani, S., et al.: Scanner invariant multiple sclerosis lesion segmentation from MRI. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE (2020)","key":"24_CR4","DOI":"10.1109\/ISBI45749.2020.9098721"},{"unstructured":"Dou, Q., de Castro, D.C., Kamnitsas, K., Glocker, B.: Domain generalization via model-agnostic learning of semantic features. In: NeurIPS, pp. 6450\u20136461 (2019)","key":"24_CR5"},{"doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., et al.: Cbam: convolutional block attention module. In: Proceedings of the ECCV (2018)","key":"24_CR6","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"24_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1007\/978-3-030-59713-9_46","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Q Liu","year":"2020","unstructured":"Liu, Q., Dou, Q., Heng, P.A.: Shape-aware meta-learning for generalizing prostate MRI segmentation to unseen domains. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 475\u2013485. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_46"},{"doi-asserted-by":"crossref","unstructured":"Zhou, K., et al.: Deep domain-adversarial image generation for domain generalisation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 07 (2020)","key":"24_CR8","DOI":"10.1609\/aaai.v34i07.7003"},{"doi-asserted-by":"crossref","unstructured":"Li, L., Zimmer, V.A., Ding, W., et al.: Random style transfer based domain generalization networks integrating shape and spatial information. arXiv preprint arXiv:2008.12205 (2020)","key":"24_CR9","DOI":"10.1007\/978-3-030-68107-4_21"},{"doi-asserted-by":"crossref","unstructured":"Yu, L., Yang, X., Chen, H., Qin, J., Heng, P.A.: Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MR images. In: AAAI (2017)","key":"24_CR10","DOI":"10.1609\/aaai.v31i1.10510"},{"doi-asserted-by":"crossref","unstructured":"Nie, D., Gao, Y., Wang, L., Shen, D.: ASDNet: attention based semi-supervised deep networks for medical image segmentation. In: International Conference on MICCAI (2018)","key":"24_CR11","DOI":"10.1007\/978-3-030-00937-3_43"},{"doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Deep attentional features for prostate segmentation in ultrasound. In: International Conference on MICCAI (2018)","key":"24_CR12","DOI":"10.1007\/978-3-030-00937-3_60"},{"doi-asserted-by":"crossref","unstructured":"Ghafoorian, M., et al.: Transfer learning for domain adaptation in MRI: application in brain lesion segmentation. In: International Conference on MICCAI (2017)","key":"24_CR13","DOI":"10.1007\/978-3-319-66179-7_59"},{"doi-asserted-by":"crossref","unstructured":"Dou, Q., Liu, Q., Heng, P.A., Glocker, B.: Unpaired multi-modal segmentation via knowledge distillation. IEEE TMI 39, 2415-2425 (2020)","key":"24_CR14","DOI":"10.1109\/TMI.2019.2963882"},{"unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning. PMLR (2015)","key":"24_CR15"},{"issue":"4","key":"24_CR16","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1109\/TMI.2015.2508280","volume":"35","author":"Y Guo","year":"2015","unstructured":"Guo, Y., Gao, Y., Shen, D.: Deformable MR prostate segmentation via deep feature learning and sparse patch matching. IEEE Trans. Med. Imaging 35(4), 1077\u20131089 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"24_CR17","first-page":"10","volume":"2018","author":"Q Zhu","year":"2018","unstructured":"Zhu, Q., Du, B., Turkbey, B., Choyke, P., Yan, P.: Exploiting interslice correlation for MRI prostate image segmentation, from recursive neural networks aspect. Complexity 2018, 10 (2018)","journal-title":"Complexity"},{"doi-asserted-by":"crossref","unstructured":"Liu, Q., Chen, C., Qin, J., et al.: Feddg: federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. In: Proceedings of the IEEE cconference on CVPR (2021)","key":"24_CR18","DOI":"10.1109\/CVPR46437.2021.00107"},{"doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Cnference on CVPR (2018)","key":"24_CR19","DOI":"10.1109\/CVPR.2018.00745"},{"doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on MICCAI (2015)","key":"24_CR20","DOI":"10.1007\/978-3-319-24574-4_28"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92270-2_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:23:32Z","timestamp":1710257012000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92270-2_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030922696","9783030922702"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92270-2_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1093","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":"226","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":"177","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":"21% - 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":"2.57","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":"6","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":"Due to the COVID-19 pandemic the conference was held online.","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)"}}]}}