{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T06:26:12Z","timestamp":1742970372793,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164392"},{"type":"electronic","value":"9783031164408"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16440-8_69","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T09:30:11Z","timestamp":1663234211000},"page":"725-735","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Harnessing Deep Bladder Tumor Segmentation with\u00a0Logical Clinical Knowledge"],"prefix":"10.1007","author":[{"given":"Xiao","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Yue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhikang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"69_CR1","doi-asserted-by":"crossref","unstructured":"Cha, K.H., et al.: Computer-aided detection of bladder masses in CT urography (CTU). In: Medical Imaging 2017: Computer-Aided Diagnosis, vol. 10134, p. 1013403. International Society for Optics and Photonics (2017)","DOI":"10.1117\/12.2255668"},{"key":"69_CR2","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"69_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"issue":"7","key":"69_CR4","doi-asserted-by":"publisher","first-page":"1609","DOI":"10.1007\/s10994-021-05966-z","volume":"110","author":"T Dash","year":"2021","unstructured":"Dash, T., Srinivasan, A., Vig, L.: Incorporating symbolic domain knowledge into graph neural networks. Mach. Learn. 110(7), 1609\u20131636 (2021). https:\/\/doi.org\/10.1007\/s10994-021-05966-z","journal-title":"Mach. Learn."},{"issue":"12","key":"69_CR5","doi-asserted-by":"publisher","first-page":"5482","DOI":"10.1002\/mp.13240","volume":"45","author":"J Dolz","year":"2018","unstructured":"Dolz, J., et al.: Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks. Med. Phys. 45(12), 5482\u20135493 (2018)","journal-title":"Med. Phys."},{"key":"69_CR6","doi-asserted-by":"publisher","first-page":"179656","DOI":"10.1109\/ACCESS.2020.3025372","volume":"8","author":"T Fan","year":"2020","unstructured":"Fan, T., Wang, G., Li, Y., Wang, H.: Ma-net: a multi-scale attention network for liver and tumor segmentation. IEEE Access 8, 179656\u2013179665 (2020)","journal-title":"IEEE Access"},{"issue":"11","key":"69_CR7","doi-asserted-by":"publisher","first-page":"5814","DOI":"10.1002\/mp.12510","volume":"44","author":"SS Garapati","year":"2017","unstructured":"Garapati, S.S., et al.: Urinary bladder cancer staging in CT urography using machine learning. Med. Phys. 44(11), 5814\u20135823 (2017)","journal-title":"Med. Phys."},{"key":"69_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2021.107510","volume":"93","author":"R Ge","year":"2021","unstructured":"Ge, R., et al.: Md-unet: Multi-input dilated u-shape neural network for segmentation of bladder cancer. Comput. Biol. Chem. 93, 107510 (2021)","journal-title":"Comput. Biol. Chem."},{"key":"69_CR9","doi-asserted-by":"crossref","unstructured":"Gosnell, M.E., Polikarpov, D.M., Goldys, E.M., Zvyagin, A.V., Gillatt, D.A.: Computer-assisted cystoscopy diagnosis of bladder cancer. In: Urologic Oncology: Seminars and Original Investigations, vol. 36, pp. 8\u2013e9. Elsevier (2018)","DOI":"10.1016\/j.urolonc.2017.08.026"},{"key":"69_CR10","doi-asserted-by":"crossref","unstructured":"Huang, X., Yue, X., Xu, Z., Chen, Y.: Integrating general and specific priors into deep convolutional neural networks for bladder tumor segmentation. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9533813"},{"key":"69_CR11","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.media.2019.02.009","volume":"54","author":"H Kervadec","year":"2019","unstructured":"Kervadec, H., Dolz, J., Tang, M., Granger, E., Boykov, Y., Ayed, I.B.: Constrained-CNN losses for weakly supervised segmentation. Med. Image Anal. 54, 88\u201399 (2019)","journal-title":"Med. Image Anal."},{"key":"69_CR12","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"69_CR13","doi-asserted-by":"crossref","unstructured":"Li, R., Chen, H., Gong, G., Wang, L.: Bladder wall segmentation in MRI images via deep learning and anatomical constraints. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1629\u20131632. IEEE (2020)","DOI":"10.1109\/EMBC44109.2020.9176112"},{"issue":"12","key":"69_CR14","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-denseunet: hybrid densely connected unet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663\u20132674 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"69_CR15","doi-asserted-by":"publisher","first-page":"1752","DOI":"10.1002\/mp.13438","volume":"46","author":"X Ma","year":"2019","unstructured":"Ma, X., et al.: U-net based deep learning bladder segmentation in CT urography. Med. Phys. 46(4), 1752\u20131765 (2019)","journal-title":"Med. Phys."},{"key":"69_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1007\/978-3-030-00937-3_84","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Z Mirikharaji","year":"2018","unstructured":"Mirikharaji, Z., Hamarneh, G.: Star shape prior in fully convolutional networks for skin lesion segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 737\u2013745. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_84"},{"key":"69_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1007\/978-3-030-00934-2_26","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"H Oda","year":"2018","unstructured":"Oda, H., et al.: BESNet: boundary-enhanced segmentation of cells in histopathological images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 228\u2013236. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_26"},{"key":"69_CR18","unstructured":"Oktay, O., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"69_CR19","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"},{"issue":"6","key":"69_CR20","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1016\/j.eururo.2019.08.032","volume":"76","author":"E Shkolyar","year":"2019","unstructured":"Shkolyar, E., Jia, X., Chang, T.C., Trivedi, D., Mach, K.E., Meng, M.Q.H., Xing, L., Liao, J.C.: Augmented bladder tumor detection using deep learning. Eur. Urol. 76(6), 714\u2013718 (2019)","journal-title":"Eur. Urol."},{"key":"69_CR21","unstructured":"Xie, Y., Xu, Z., Kankanhalli, M.S., Meel, K.S., Soh, H.: Embedding symbolic knowledge into deep networks. Advances in neural information processing systems 32 (2019)"},{"key":"69_CR22","doi-asserted-by":"publisher","first-page":"7141","DOI":"10.1109\/TIP.2020.2998981","volume":"29","author":"S Yan","year":"2020","unstructured":"Yan, S., Tai, X.C., Liu, J., Huang, H.Y.: Convexity shape prior for level set-based image segmentation method. IEEE Trans. Image Process. 29, 7141\u20137152 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"69_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1007\/978-3-030-32245-8_62","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Q Yue","year":"2019","unstructured":"Yue, Q., Luo, X., Ye, Q., Xu, L., Zhuang, X.: Cardiac segmentation from LGE MRI using deep neural network incorporating shape and spatial priors. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 559\u2013567. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_62"},{"key":"69_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, C., Yue, X., Chen, Y., Lv, Y.: Integrating diagnosis rules into deep neural networks for bladder cancer staging. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2301\u20132304 (2020)","DOI":"10.1145\/3340531.3412122"},{"issue":"5","key":"69_CR25","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual u-net. IEEE Geosci. Remote Sens. Lett. 15(5), 749\u2013753 (2018)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"69_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., Taylor, Z., Carneiro, G., Syeda-Mahmood, T., Martel, A., Maier-Hein, L., Tavares, J.M.R.S., Bradley, A., Papa, J.P., Belagiannis, V., Nascimento, J.C., Lu, Z., Conjeti, S., Moradi, M., Greenspan, H., Madabhushi, A. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16440-8_69","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T18:14:06Z","timestamp":1711563246000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16440-8_69"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164392","9783031164408"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16440-8_69","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2022\/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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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":"5","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)"}}]}}