{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T21:59:22Z","timestamp":1755035962927,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030913861"},{"type":"electronic","value":"9783030913878"}],"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\/978-3-030-91387-8_6","type":"book-chapter","created":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T15:05:39Z","timestamp":1637247939000},"page":"81-96","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Efficient Brain Hemorrhage Detection on\u00a03D CT Scans with Deep Neural Network"],"prefix":"10.1007","author":[{"given":"Anh-Cang","family":"Phan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ho-Dat","family":"Tran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thuong-Cang","family":"Phan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,19]]},"reference":[{"key":"6_CR1","unstructured":"Brownlee, J.: How to use learning curves to diagnose machine learning model performance. Mach. Learn. Mastery (2019)"},{"key":"6_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"},{"issue":"1","key":"6_CR3","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","volume":"16","author":"T Falk","year":"2019","unstructured":"Falk, T., et al.: U-net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16(1), 67\u201370 (2019)","journal-title":"Nat. Methods"},{"key":"6_CR4","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256. JMLR Workshop and Conference Proceedings (2010)"},{"issue":"4","key":"6_CR5","doi-asserted-by":"publisher","first-page":"1514","DOI":"10.1109\/JBHI.2014.2356402","volume":"19","author":"M Gonzalez-Hidalgo","year":"2014","unstructured":"Gonzalez-Hidalgo, M., Guerrero-Pena, F., Herold-Garc\u00eda, S., Jaume-i Cap\u00f3, A., Marrero-Fern\u00e1ndez, P.D.: Red blood cell cluster separation from digital images for use in sickle cell disease. IEEE J. Biomed. Health Inform. 19(4), 1514\u20131525 (2014)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"6_CR6","unstructured":"Hssayeni, M.: Computed tomography images for intracranial hemorrhage detection and segmentation. PhysioNet (2019)"},{"issue":"1","key":"6_CR7","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/data5010014","volume":"5","author":"MD Hssayeni","year":"2020","unstructured":"Hssayeni, M.D., Croock, M.S., Salman, A.D., Al-khafaji, H.F., Yahya, Z.A., Ghoraani, B.: Intracranial hemorrhage segmentation using a deep convolutional model. Data 5(1), 14 (2020)","journal-title":"Data"},{"issue":"43","key":"6_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.17485\/ijst\/2016\/v9i43\/95454","volume":"9","author":"R Janani","year":"2016","unstructured":"Janani, R., Vijayarani, S.: An efficient text pattern matching algorithm for retrieving information from desktop. Indian J. Sci. Technol. 9(43), 1\u201311 (2016)","journal-title":"Indian J. Sci. Technol."},{"key":"6_CR9","unstructured":"Kohl, S.A., et al.: A probabilistic U-net for segmentation of ambiguous images. arXiv preprint arXiv:1806.05034 (2018)"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Labati, R.D., Piuri, V., Scotti, F.: All-IDB: the acute lymphoblastic leukemia image database for image processing. In: 2011 18th IEEE International Conference on Image Processing, pp. 2045\u20132048. IEEE (2011)","DOI":"10.1109\/ICIP.2011.6115881"},{"key":"6_CR11","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-3-030-62324-1_3","volume-title":"Computational Intelligence Methods for Green Technology and Sustainable Development","author":"KG Luong","year":"2021","unstructured":"Luong, K.G., et al.: A computer-aided detection to intracranial hemorrhage by using deep learning: a case study. In: Huang, Y.-P., Wang, W.-J., Quoc, H.A., Giang, L.H., Hung, N.-L. (eds.) GTSD 2020. AISC, vol. 1284, pp. 27\u201338. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-62324-1_3"},{"key":"6_CR12","unstructured":"Oktay, O., et al.: Attention U-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"6_CR13","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/978-981-33-4370-2_20","volume-title":"Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications","author":"A-C Phan","year":"2020","unstructured":"Phan, A.-C., Cao, H.-P., Trieu, T.-N., Phan, T.-C.: Detection and classification of brain hemorrhage using hounsfield unit and deep learning techniques. In: Dang, T.K., K\u00fcng, J., Takizawa, M., Chung, T.M. (eds.) FDSE 2020. CCIS, vol. 1306, pp. 281\u2013293. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-33-4370-2_20"},{"key":"6_CR14","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":"6_CR15","unstructured":"Roy, P., Ghosh, S., Bhattacharya, S., Pal, U.: Effects of degradations on deep neural network architectures. arXiv preprint arXiv:1807.10108 (2018)"},{"issue":"5","key":"6_CR16","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1109\/TMI.2016.2525803","volume":"35","author":"K Sirinukunwattana","year":"2016","unstructured":"Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.W., Snead, D.R., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196\u20131206 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"6_CR17","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":"6_CR18","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"}],"container-title":["Lecture Notes in Computer Science","Future Data and Security Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91387-8_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,22]],"date-time":"2021-11-22T00:11:57Z","timestamp":1637539917000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91387-8_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030913861","9783030913878"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91387-8_6","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":"19 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FDSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Future Data and Security Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fdse2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/thefdse.org\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"168","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":"52","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":"8","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":"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)"}}]}}