{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T07:57:53Z","timestamp":1768550273212,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":16,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819604364","type":"print"},{"value":"9789819604371","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-96-0437-1_8","type":"book-chapter","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T16:56:56Z","timestamp":1732640216000},"page":"103-116","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detection and\u00a0Evaluation of\u00a0Ki-67 Proliferation Index of\u00a0Breast Cancer Cells Using Deep Learning Technique"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1470-5496","authenticated-orcid":false,"given":"Anh-Cang","family":"Phan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thi-My-Tien","family":"Le","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minh-Sang","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","unstructured":"Baban A\u00a0Erep, T.R., Chaari, L.: Mid-DeepLabv3+: a novel approach for image semantic segmentation applied to African food dietary assessments. Sensors (Basel) 24(1), 209 (2023). https:\/\/doi.org\/10.3390\/s24010209","DOI":"10.3390\/s24010209"},{"issue":"17","key":"8_CR2","doi-asserted-by":"publisher","first-page":"4455","DOI":"10.3390\/cancers13174455","volume":"13","author":"MG Davey","year":"2021","unstructured":"Davey, M.G., Hynes, S.O., Kerin, M.J., Miller, N., Lowery, A.J.: Ki-67 as a prognostic biomarker in invasive breast cancer. Cancers (Basel) 13(17), 4455 (2021). https:\/\/doi.org\/10.3390\/cancers13174455","journal-title":"Cancers (Basel)"},{"key":"8_CR3","doi-asserted-by":"publisher","unstructured":"Dolz, J., Desrosiers, C., Ben\u00a0Ayed, I.: IVD-net: intervertebral disc localization and segmentation in MRI with a multi-modal UNet. In: Lecture Notes in Computer Science, pp. 130\u2013143. Lecture notes in computer science, Springer International Publishing, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-13736-6-11","DOI":"10.1007\/978-3-030-13736-6-11"},{"issue":"1","key":"8_CR4","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.ymeth.2019.06.014","volume":"1","author":"P Gamble","year":"2021","unstructured":"Gamble, P., et al.: Determining breast cancer biomarker status and associated morphological features using deep learning. Commun. Med. (Lond.) 1(1), 14 (2021). https:\/\/doi.org\/10.1016\/j.ymeth.2019.06.014","journal-title":"Commun. Med. (Lond.)"},{"issue":"4","key":"8_CR5","doi-asserted-by":"publisher","first-page":"727","DOI":"10.3233\/xst-200658","volume":"28","author":"Y Hou","year":"2020","unstructured":"Hou, Y.: Breast cancer pathological image classification based on deep learning. J. Xray Sci. Technol. 28(4), 727\u2013738 (2020). https:\/\/doi.org\/10.3233\/xst-200658","journal-title":"J. Xray Sci. Technol."},{"issue":"3","key":"8_CR6","doi-asserted-by":"publisher","first-page":"224","DOI":"10.5204\/mcj.1512","volume":"74","author":"N Jokhadze","year":"2024","unstructured":"Jokhadze, N., Das, A., Dizon, D.S.: Global cancer statistics: a healthy population relies on population health. CA Cancer J. Clin. 74(3), 224\u2013226 (2024). https:\/\/doi.org\/10.5204\/mcj.1512","journal-title":"CA Cancer J. Clin."},{"issue":"1","key":"8_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-86912-w","volume":"11","author":"F Negahbani","year":"2021","unstructured":"Negahbani, F., et al.: PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer. Sci. Rep. 11(1), 1\u201313 (2021). https:\/\/doi.org\/10.1038\/s41598-021-86912-w","journal-title":"Sci. Rep."},{"issue":"1","key":"8_CR8","doi-asserted-by":"publisher","first-page":"8489","DOI":"10.1038\/s41598-021-86912-w","volume":"11","author":"F Negahbani","year":"2021","unstructured":"Negahbani, F., et al.: PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer. Sci. Rep. 11(1), 8489 (2021). https:\/\/doi.org\/10.1038\/s41598-021-86912-w","journal-title":"Sci. Rep."},{"issue":"3","key":"8_CR9","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1111\/j.1365-2184.2007.00433.x","volume":"40","author":"R Rahmanzadeh","year":"2007","unstructured":"Rahmanzadeh, R., H\u00fcttmann, G., Gerdes, J., Scholzen, T.: Chromophore-assisted light inactivation of pKi-67 leads to inhibition of ribosomal RNA synthesis. Cell Prolif. 40(3), 422\u2013430 (2007). https:\/\/doi.org\/10.1111\/j.1365-2184.2007.00433.x","journal-title":"Cell Prolif."},{"key":"8_CR10","doi-asserted-by":"publisher","unstructured":"Saha, M., et al.: Histogram based thresholding for automated nucleus segmentation using breast imprint cytology. In: Advancements of Medical Electronics, pp. 49\u201357. Springer India, New Delhi (2015). https:\/\/doi.org\/10.1007\/978-81-322-2256-9-5","DOI":"10.1007\/978-81-322-2256-9-5"},{"issue":"3","key":"8_CR11","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1002\/(SICI)1097-4652(200003)182:3<311::AID-JCP1>3.0.CO;2-9","volume":"182","author":"T Scholzen","year":"2000","unstructured":"Scholzen, T., Gerdes, J.: The Ki-67 protein: from the known and the unknown. J. Cell. Physiol. 182(3), 311\u2013322 (2000)","journal-title":"J. Cell. Physiol."},{"key":"8_CR12","doi-asserted-by":"publisher","unstructured":"Schonk, D.M., et al.: Assignment of the gene(s) involved in the expression of the proliferation-related Ki-67 antigen to human chromosome 10. Hum. Genet. 83(3), 297\u2013299 (1989). https:\/\/doi.org\/10.1007\/BF00285178","DOI":"10.1007\/BF00285178"},{"key":"8_CR13","doi-asserted-by":"publisher","unstructured":"Singh, S., Kumar, R.: Histopathological image analysis for breast cancer detection using cubic SVM. In: 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE (2020). https:\/\/doi.org\/10.1109\/SPIN48934.2020.9071218","DOI":"10.1109\/SPIN48934.2020.9071218"},{"key":"8_CR14","doi-asserted-by":"publisher","unstructured":"Thunuguntla, S.B., Murugaanandam, S., Pitchai, R.: Densenet121-DNN-based hybrid approach for advertisement classification and user identification. Int. J. Intell. Eng. Syst. 16, 162\u2013174 (2023). https:\/\/doi.org\/10.22266\/ijies2023.0630.13","DOI":"10.22266\/ijies2023.0630.13"},{"key":"8_CR15","doi-asserted-by":"publisher","unstructured":"Xie, W., Noble, J.A., Zisserman, A.: Microscopy cell counting and detection with fully convolutional regression networks. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 6(3), 283\u2013292 (2018). https:\/\/doi.org\/10.1080\/21681163.2016.1149104","DOI":"10.1080\/21681163.2016.1149104"},{"key":"8_CR16","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.ymeth.2019.06.014","volume":"173","author":"R Yan","year":"2020","unstructured":"Yan, R., et al.: Breast cancer histopathological image classification using a hybrid deep neural network. Methods 173, 52\u201360 (2020). https:\/\/doi.org\/10.1016\/j.ymeth.2019.06.014","journal-title":"Methods"}],"container-title":["Communications in Computer and Information Science","Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0437-1_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T17:03:03Z","timestamp":1732640583000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0437-1_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819604364","9789819604371"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0437-1_8","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"27 November 2024","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":"Binh Duong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fdse2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/thefdse.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}