{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T12:04:37Z","timestamp":1790510677164,"version":"4.1.0"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030597092","type":"print"},{"value":"9783030597108","type":"electronic"}],"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\/978-3-030-59710-8_50","type":"book-chapter","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T18:06:41Z","timestamp":1601575601000},"page":"510-520","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation"],"prefix":"10.1007","author":[{"given":"Eleni","family":"Chiou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Giganti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shonit","family":"Punwani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iasonas","family":"Kokkinos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleftheria","family":"Panagiotaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"50_CR1","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1016\/S0140-6736(16)32401-1","volume":"389","author":"HU Ahmed","year":"2017","unstructured":"Ahmed, H.U., Bosaily, A., et al.: Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 389, 815\u2013822 (2017)","journal-title":"Lancet"},{"key":"50_CR2","first-page":"174","volume":"52","author":"J Cai","year":"2019","unstructured":"Cai, J., Zhang, Z., Cui, L., Zheng, Y., Yang, L.: Towards cross-modal organ translation and segmentation: a cycle and shape consistent generative adversarial network. MedIA 52, 174\u2013184 (2019)","journal-title":"MedIA"},{"key":"50_CR3","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"50_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/978-3-030-00919-9_37","volume-title":"Machine Learning in Medical Imaging","author":"E Chiou","year":"2018","unstructured":"Chiou, E., Giganti, F., Bonet-Carne, E., Punwani, S., Kokkinos, I., Panagiotaki, E.: Prostate cancer classification on VERDICT DW-MRI using convolutional neural networks. In: Shi, Y., Suk, H.-I., Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 319\u2013327. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00919-9_37"},{"key":"50_CR5","doi-asserted-by":"crossref","unstructured":"Chiou, E., Giganti, F., Punwani, S., Kokkinos, I., Panagiotaki, E.: Domain adaptation for prostate lesion segmentation on VERDICT-MRI. In: ISMRM (2020)","DOI":"10.1007\/978-3-030-59710-8_50"},{"key":"50_CR6","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: NIPS (2014)"},{"key":"50_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"50_CR8","unstructured":"Hoffman, J., et al.: CyCADA: cycle-consistent adversarial domain adaptation. In: ICML (2018)"},{"key":"50_CR9","doi-asserted-by":"crossref","unstructured":"Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.167"},{"key":"50_CR10","doi-asserted-by":"crossref","unstructured":"Huang, X., Liu, M.Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"50_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1007\/978-3-030-00934-2_86","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"J Jiang","year":"2018","unstructured":"Jiang, J., et al.: Tumor-aware, adversarial domain adaptation from CT to MRI for lung cancer segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 777\u2013785. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_86"},{"key":"50_CR12","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1148\/radiol.2019181749","volume":"291","author":"EW Johnston","year":"2019","unstructured":"Johnston, E.W., Bonet-Carne, E., et al.: VERDICT-MRI for prostate cancer: intracellular volume fraction versus apparent diffusion coefficient. Radiology 291, 391\u2013397 (2019)","journal-title":"Radiology"},{"key":"50_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/978-3-319-59050-9_47","volume-title":"Information Processing in Medical Imaging","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas, K., et al.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 597\u2013609. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_47"},{"key":"50_CR14","doi-asserted-by":"crossref","unstructured":"Lee, H.Y., Tseng, H.Y., Huang, J.B., Singh, M., Yang, M.H.: Diverse image-to-image translation via disentangled representations. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01246-5_3"},{"key":"50_CR15","first-page":"1083","volume":"33","author":"G Litjens","year":"2014","unstructured":"Litjens, G., Debats, O., Barentsz, J., Karssemeijer, N., Huisman, H.: Computer-aided detection of prostate cancer in MRI. TMI 33, 1083\u20131092 (2014)","journal-title":"TMI"},{"key":"50_CR16","unstructured":"Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: NIPS (2017)"},{"key":"50_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1007\/978-3-030-32245-8_74","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"C Ouyang","year":"2019","unstructured":"Ouyang, C., Kamnitsas, K., Biffi, C., Duan, J., Rueckert, D.: Data efficient unsupervised domain adaptation for cross-modality image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 669\u2013677. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_74"},{"key":"50_CR18","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1097\/RLI.0000000000000115","volume":"50","author":"E Panagiotaki","year":"2015","unstructured":"Panagiotaki, E., Chan, R.W., Dikaios, N., et al.: Microstructural characterization of normal and malignant human prostate tissue with vascular, extracellular, and restricted diffusion for cytometry in tumours magnetic resonance imaging. Investigate Radiol. 50, 218\u2013227 (2015)","journal-title":"Investigate Radiol."},{"key":"50_CR19","unstructured":"Panagiotaki, E., Ianus, A., Johnston, E., et al.: Optimised VERDICT MRI protocol for prostate cancer characterisation. In: ISMRM (2015)"},{"key":"50_CR20","doi-asserted-by":"publisher","first-page":"1902","DOI":"10.1158\/0008-5472.CAN-13-2511","volume":"74","author":"E Panagiotaki","year":"2014","unstructured":"Panagiotaki, E., Walker-Samuel, S., Siow, B., et al.: Noninvasive quantification of solid tumor microstructure using VERDICT MRI. Cancer Res. 74, 1902\u20131912 (2014)","journal-title":"Cancer Res."},{"key":"50_CR21","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., et al.: Automatic differentiation in pytorch. In: Autodiff Workshop, NIPS (2017)"},{"key":"50_CR22","doi-asserted-by":"crossref","unstructured":"Rebuffi, S., Vedaldi, A., Bilen, H.: Efficient parametrization of multi-domain deep neural networks. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00847"},{"key":"50_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/978-3-030-00934-2_23","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"J Ren","year":"2018","unstructured":"Ren, J., Hacihaliloglu, I., Singer, E.A., Foran, D.J., Qi, X.: Adversarial domain adaptation for\u00a0classification of prostate histopathology whole-slide images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 201\u2013209. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_23"},{"key":"50_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"50_CR25","doi-asserted-by":"crossref","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.S.: Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.437"},{"key":"50_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/978-3-030-32245-8_29","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Yang","year":"2019","unstructured":"Yang, J., Dvornek, N.C., Zhang, F., Chapiro, J., Lin, M.D., Duncan, J.S.: Unsupervised domain adaptation via disentangled representations: application to cross-modality liver segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 255\u2013263. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_29"},{"key":"50_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/978-3-030-00934-2_67","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Miao, S., Mansi, T., Liao, R.: Task driven generative modeling for unsupervised domain adaptation: application to X-ray image segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 599\u2013607. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_67"},{"key":"50_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yang, L., Zheng, Y.: Translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00963"},{"key":"50_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, J., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"50_CR30","unstructured":"Zhu, J.Y., et al.: Toward multimodal image-to-image translation. In: NIPS (2017)"}],"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\/978-3-030-59710-8_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:03:48Z","timestamp":1759356228000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59710-8_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597092","9783030597108"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59710-8_50","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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)"}}]}}