{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T07:22:38Z","timestamp":1770880958589,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164361","type":"print"},{"value":"9783031164378","type":"electronic"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16437-8_75","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T18:13:04Z","timestamp":1663265584000},"page":"780-790","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Moving from\u00a02D to\u00a03D: Volumetric Medical Image Classification for\u00a0Rectal Cancer Staging"],"prefix":"10.1007","author":[{"given":"Joohyung","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jieun","family":"Oh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Inkyu","family":"Shin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"You-sung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dae Kyung","family":"Sohn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tae-sung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"In So","family":"Kweon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"issue":"1\u20132","key":"75_CR1","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1111\/ans.16509","volume":"91","author":"ZH Ang","year":"2021","unstructured":"Ang, Z.H., De Robles, M.S., Kang, S., Winn, R.: Accuracy of pelvic magnetic resonance imaging in local staging for rectal cancer: a single local health district, real world experience. ANZ J. Surg. 91(1\u20132), 111\u2013116 (2021)","journal-title":"ANZ J. Surg."},{"key":"75_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"key":"75_CR3","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"75_CR4","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","volume":"32","author":"MH Hesamian","year":"2019","unstructured":"Hesamian, M.H., Jia, W., He, X., Kennedy, P.: Deep learning techniques for medical image segmentation: achievements and challenges. J. Digit. Imaging 32(4), 582\u2013596 (2019). https:\/\/doi.org\/10.1007\/s10278-019-00227-x","journal-title":"J. Digit. Imaging"},{"key":"75_CR5","doi-asserted-by":"crossref","unstructured":"Horvat, N., Carlos Tavares Rocha, C., Clemente Oliveira, B., Petkovska, I., Gollub, M.J.: MRI of rectal cancer: tumor staging, imaging techniques, and management. Radiographics 39(2), 367\u2013387 (2019)","DOI":"10.1148\/rg.2019180114"},{"key":"75_CR6","doi-asserted-by":"crossref","unstructured":"Howard, A., et al.: Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"issue":"3","key":"75_CR7","doi-asserted-by":"publisher","first-page":"764","DOI":"10.3390\/s21030764","volume":"21","author":"Z Huang","year":"2021","unstructured":"Huang, Z., Zhou, Q., Zhu, X., Zhang, X.: Batch similarity based triplet loss assembled into light-weighted convolutional neural networks for medical image classification. Sensors 21(3), 764 (2021)","journal-title":"Sensors"},{"key":"75_CR8","doi-asserted-by":"crossref","unstructured":"Isensee, F., et al.: nnU-Net: self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:1809.10486 (2018)","DOI":"10.1007\/978-3-658-25326-4_7"},{"issue":"3","key":"75_CR9","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1002\/ima.22311","volume":"29","author":"J Kim","year":"2019","unstructured":"Kim, J., et al.: Rectal cancer: toward fully automatic discrimination of T2 and T3 rectal cancers using deep convolutional neural network. Int. J. Imaging Syst. Technol. 29(3), 247\u2013259 (2019)","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"75_CR10","doi-asserted-by":"publisher","first-page":"182725","DOI":"10.1109\/ACCESS.2019.2960371","volume":"7","author":"J Lee","year":"2019","unstructured":"Lee, J., Oh, J.E., Kim, M.J., Hur, B.Y., Sohn, D.K.: Reducing the model variance of a rectal cancer segmentation network. IEEE Access 7, 182725\u2013182733 (2019)","journal-title":"IEEE Access"},{"key":"75_CR11","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"issue":"6","key":"75_CR12","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1109\/TPAMI.2017.2723400","volume":"40","author":"TY Lin","year":"2017","unstructured":"Lin, T.Y., RoyChowdhury, A., Maji, S.: Bilinear convolutional neural networks for fine-grained visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1309\u20131322 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"75_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1007\/978-3-030-00934-2_94","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"S Liu","year":"2018","unstructured":"Liu, S., et al.: 3D anisotropic hybrid network: transferring convolutional features from 2D images to 3D anisotropic volumes. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 851\u2013858. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_94"},{"key":"75_CR14","doi-asserted-by":"crossref","unstructured":"Maas, M., et al.: T-staging of rectal cancer: accuracy of 3.0 tesla MRI compared with 1.5 tesla. Abdom. Imaging 37(3), 475\u2013481 (2012). DOIurl10.1007\/s00261-011-9770-5","DOI":"10.1007\/s00261-011-9770-5"},{"key":"75_CR15","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"75_CR16","doi-asserted-by":"crossref","unstructured":"Peng, C., Lin, W.A., Liao, H., Chellappa, R., Zhou, S.K.: SAINT: spatially aware interpolation network for medical slice synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7750\u20137759 (2020)","DOI":"10.1109\/CVPR42600.2020.00777"},{"key":"75_CR17","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"75_CR18","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6450\u20136459 (2018)","DOI":"10.1109\/CVPR.2018.00675"},{"issue":"1","key":"75_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-05728-9","volume":"7","author":"S Trebeschi","year":"2017","unstructured":"Trebeschi, S., et al.: Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR. Sci. Rep. 7(1), 1\u20139 (2017)","journal-title":"Sci. Rep."},{"issue":"6","key":"75_CR20","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310\u20131320 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"75_CR21","doi-asserted-by":"publisher","first-page":"2740","DOI":"10.1109\/TPAMI.2018.2868668","volume":"41","author":"L Wang","year":"2018","unstructured":"Wang, L., et al.: Temporal segment networks for action recognition in videos. IEEE Trans. Pattern Anal. Mach. Intell. 41(11), 2740\u20132755 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"75_CR22","unstructured":"Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10(2) (2009)"},{"key":"75_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/978-3-319-46478-7_31","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Y Wen","year":"2016","unstructured":"Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499\u2013515. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_31"},{"issue":"4","key":"75_CR24","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.1109\/TMI.2019.2945514","volume":"39","author":"N Wu","year":"2019","unstructured":"Wu, N., et al.: Deep neural networks improve radiologists\u2019 performance in breast cancer screening. IEEE Trans. Med. Imaging 39(4), 1184\u20131194 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"75_CR25","unstructured":"Zheng, H., Fu, J., Zha, Z.J., Luo, J.: Learning deep bilinear transformation for fine-grained image representation. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"}],"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\/978-3-031-16437-8_75","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T14:12:45Z","timestamp":1710252765000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16437-8_75"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164361","9783031164378"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16437-8_75","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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":"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)"}}]}}