{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:13:07Z","timestamp":1743091987037,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811989902"},{"type":"electronic","value":"9789811989919"}],"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\/978-981-19-8991-9_5","type":"book-chapter","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T08:04:02Z","timestamp":1674029042000},"page":"57-69","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Multi-module 3D U-Net Learning Architecture for\u00a0Brain Tumor Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7863-0115","authenticated-orcid":false,"given":"Saqib","family":"Ali","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1995-9249","authenticated-orcid":false,"given":"Jianqiang","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1545-9204","authenticated-orcid":false,"given":"Yan","family":"Pei","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1941-9085","authenticated-orcid":false,"given":"Khalil Ur","family":"Rehman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Qamar, S., Jin, H., Zheng, R., Ahmad, P.: 3D hyper-dense connected convolutional neural network for brain tumor segmentation. In: 2018 14th International Conference on Semantics, Knowledge and Grids (SKG), 12\u201314 September 2018, Guangzhou, China, pp. 123\u2013130 (2018)","DOI":"10.1109\/SKG.2018.00024"},{"issue":"104171","key":"5_CR2","first-page":"1","volume":"206","author":"JN Sua","year":"2020","unstructured":"Sua, J.N., et al.: Incorporating convolutional neural networks and sequence graph transform for identifying multilabel protein Lysine PTM sites. Chemom. Intell. Lab. Syst. 206(104171), 1\u20138 (2020)","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"5_CR3","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.neucom.2019.09.070","volume":"375","author":"NQK Le","year":"2020","unstructured":"Le, N.Q.K., Ho, Q.-T., Yapp, E.K.Y., Ou, Y.-Y., Yeh, H.-Y.: DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain\u2019s complexes. Neurocomputing 375, 71\u201379 (2020)","journal-title":"Neurocomputing"},{"key":"5_CR4","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18\u201331 (2017)","journal-title":"Med. Image Anal."},{"key":"5_CR5","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61\u201378 (2017)","journal-title":"Med. Image Anal."},{"key":"5_CR6","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":"5_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/978-3-319-75238-9_25","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"F Isensee","year":"2018","unstructured":"Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 287\u2013297. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75238-9_25"},{"key":"5_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.media.2018.10.004","volume":"51","author":"M Khened","year":"2019","unstructured":"Khened, M., Kollerathu, V., Krishnamurthi, G.: Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Med. Image Anal. 51, 21\u201345 (2019)","journal-title":"Med. Image Anal."},{"key":"5_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/978-3-030-87193-2_11","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"W Wang","year":"2021","unstructured":"Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: multimodal brain tumor segmentation using transformer. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 109\u2013119. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_11"},{"key":"5_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2020.101787","volume":"65","author":"G Wang","year":"2020","unstructured":"Wang, G., Song, T., Dong, Q., Cui, M., Huang, N., Zhang, S.: Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks. Med. Image Anal. 65, 1\u201314 (2020)","journal-title":"Med. Image Anal."},{"key":"5_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1007\/978-3-030-72084-1_43","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"M Ghaffari","year":"2021","unstructured":"Ghaffari, M., Sowmya, A., Oliver, R.: Automated brain tumour segmentation using cascaded 3D densely-connected U-net. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12658, pp. 481\u2013491. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72084-1_43"},{"key":"5_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1007\/978-3-030-46643-5_9","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"U Baid","year":"2020","unstructured":"Baid, U., Shah, N.A., Talbar, S.: Brain tumor segmentation with cascaded deep convolutional neural network. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11993, pp. 90\u201398. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46643-5_9"},{"key":"5_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-3-030-46640-4_17","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"MH Vu","year":"2020","unstructured":"Vu, M.H., Nyholm, T., L\u00f6fstedt, T.: TuNet: end-to-end hierarchical brain tumor segmentation using cascaded networks. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 174\u2013186. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46640-4_17"},{"key":"5_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1007\/978-3-030-46640-4_19","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"S Kim","year":"2020","unstructured":"Kim, S., Luna, M., Chikontwe, P., Park, S.H.: Two-step U-nets for brain tumor segmentation and random forest with radiomics for survival time prediction. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 200\u2013209. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46640-4_19"},{"key":"5_CR15","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, Las Vegas, NV, USA, 27\u201330 June 2016, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR16","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., et al. (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"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings Of The IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7\u201312 June 2015, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, California USA, 4\u20139 February 2017 (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"5_CR19","unstructured":"Kayalibay, B., Jensen, G., Smagt, P. CNN-based segmentation of medical imaging data. ArXiv Preprint ArXiv:1701.03056, pp. 1\u201324 (2017)"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Chen, X., Liew, J., Xiong, W., Chui, C., Ong, S.: Focus, segment and erase: an efficient network for multi-label brain tumor segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), Munich Germany, 8\u201314 September 2018, pp. 654\u2013669 (2018)","DOI":"10.1007\/978-3-030-01261-8_40"},{"key":"5_CR21","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.media.2017.10.002","volume":"43","author":"X Zhao","year":"2018","unstructured":"Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., Fan, Y.: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med. Image Anal. 43, 98\u2013111 (2018)","journal-title":"Med. Image Anal."},{"key":"5_CR22","doi-asserted-by":"publisher","first-page":"4516","DOI":"10.1109\/TIP.2020.2973510","volume":"29","author":"C Zhou","year":"2020","unstructured":"Zhou, C., Ding, C., Wang, X., Lu, Z., Tao, D.: One-pass multi-task networks with cross-task guided attention for brain tumor segmentation. IEEE Trans. Image Process. 29, 4516\u20134529 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"5_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1007\/978-3-030-72087-2_18","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"L Fidon","year":"2021","unstructured":"Fidon, L., Ourselin, S., Vercauteren, T.: Generalized wasserstein dice score, distributionally robust deep learning, and ranger for brain tumor segmentation: BraTS 2020 challenge. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12659, pp. 200\u2013214. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72087-2_18"},{"key":"5_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1007\/978-3-030-46640-4_20","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"Y-X Zhao","year":"2020","unstructured":"Zhao, Y.-X., Zhang, Y.-M., Liu, C.-L.: Bag of tricks for 3D MRI brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 210\u2013220. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46640-4_20"},{"key":"5_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-3-030-46640-4_36","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"R McKinley","year":"2020","unstructured":"McKinley, R., Rebsamen, M., Meier, R., Wiest, R.: Triplanar ensemble of 3D-to-2D CNNs with label-uncertainty for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 379\u2013387. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46640-4_36"},{"key":"5_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1007\/978-3-030-46643-5_32","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"D Lachinov","year":"2020","unstructured":"Lachinov, D., Shipunova, E., Turlapov, V.: Knowledge distillation for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11993, pp. 324\u2013332. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46643-5_32"}],"container-title":["Communications in Computer and Information Science","Data Mining and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-8991-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T08:10:31Z","timestamp":1674029431000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-8991-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811989902","9789811989919"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-8991-9_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DMBD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Mining and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"21 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dmbd2022","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"135","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":"62","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":"46% - 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":"2.8","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":"2-3","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)"}}]}}