{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:01:29Z","timestamp":1743026489227,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031440960"},{"type":"electronic","value":"9783031440977"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44097-7_8","type":"book-chapter","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T09:02:22Z","timestamp":1695373342000},"page":"80-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification of\u00a0the\u00a0Problem of\u00a0Neural Network Stability in\u00a0Breast Cancer Classification by\u00a0Histological Micrographs"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7950-1073","authenticated-orcid":false,"given":"Dmitry","family":"Sasov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4854-7462","authenticated-orcid":false,"given":"Yulia","family":"Orlova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3086-4929","authenticated-orcid":false,"given":"Anastasia","family":"Donsckaia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0425-5695","authenticated-orcid":false,"given":"Alexander","family":"Zubkov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5767-6810","authenticated-orcid":false,"given":"Anna","family":"Kuznetsova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0795-2675","authenticated-orcid":false,"given":"Victor","family":"Noskin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,23]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","unstructured":"Ara\u00fajo, T., et al.: Classification of breast cancer histology images using convolutional neural networks. PLOS ONE 12(6), 1\u201314 (06 2017). https:\/\/doi.org\/10.1371\/journal.pone.0177544","DOI":"10.1371\/journal.pone.0177544"},{"key":"8_CR2","unstructured":"Breast cancer statistics and resources. https:\/\/www.bcrf.org\/breast-cancer-statistics-and-resources. Accessed 01 May 2023"},{"key":"8_CR3","unstructured":"Different types of CNN models. https:\/\/iq.opengenus.org\/different-types-of-cnn-models\/. Accessed 05 Apr 2023"},{"key":"8_CR4","doi-asserted-by":"publisher","first-page":"126","DOI":"10.3390\/diagnostics13010126","volume":"13","author":"A Bagchi","year":"2022","unstructured":"Bagchi, A., Pramanik, P., Sarkar, R.: A multi-stage approach to breast cancer classification using histopathology images. Diagnostics 13, 126 (2022). https:\/\/doi.org\/10.3390\/diagnostics13010126","journal-title":"Diagnostics"},{"key":"8_CR5","doi-asserted-by":"publisher","unstructured":"Bhushan, A., Gonsalves, A., Menon, J.U.: Current state of breast cancer diagnosis, treatment, and theranostics. Pharmaceutics 13(5) (2021). https:\/\/doi.org\/10.3390\/pharmaceutics13050723, https:\/\/www.mdpi.com\/1999-4923\/13\/5\/723","DOI":"10.3390\/pharmaceutics13050723"},{"key":"8_CR6","first-page":"25","volume":"3","author":"A Borbat","year":"2020","unstructured":"Borbat, A., Lishchuk, S.: Pervyj rossijskij nabor dannyh gistologicheskih izobrazhenij patologicheskih processov molochnoj zhelezy. Vrach Inform. Tekhnol. 3, 25\u201330 (2020). in Russ","journal-title":"Vrach Inform. Tekhnol."},{"key":"8_CR7","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1016\/j.procs.2020.03.427","volume":"167","author":"K Gupta","year":"2020","unstructured":"Gupta, K., Chawla, N.: Analysis of histopathological images for prediction of breast cancer using traditional classifiers with pre-trained CNN. Procedia Comput. Sci. 167, 878\u2013889 (2020). https:\/\/doi.org\/10.1016\/j.procs.2020.03.427","journal-title":"Procedia Comput. Sci."},{"key":"8_CR8","doi-asserted-by":"publisher","unstructured":"Hameed, Z., Zahia, S., Garcia-Zapirain, B., Javier Aguirre, J., Mar\u00eda Vanegas, A.: Breast cancer histopathology image classification using an ensemble of deep learning models. Sensors 20(16) (2020). https:\/\/doi.org\/10.3390\/s20164373, https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4373","DOI":"10.3390\/s20164373"},{"key":"8_CR9","unstructured":"Kassani, H.S., Hosseinzadeh Kassani, P., Wesolowski, M., Schneider, K., Deters, R.: Classification of histopathological biopsy images using ensemble of deep learning networks, p. 8 (2019)"},{"key":"8_CR10","doi-asserted-by":"publisher","unstructured":"Li, X., Shen, X., Zhou, Y., Wang, X., Li, T.Q.: Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet). PLOS ONE 15(5), 1\u201313 (05 2020). https:\/\/doi.org\/10.1371\/journal.pone.0232127","DOI":"10.1371\/journal.pone.0232127"},{"key":"8_CR11","doi-asserted-by":"publisher","unstructured":"Sarvamangala, D.R., Kulkarni, R.V.: Convolutional neural networks in medical image understanding: a survey. Evol. Intell. 15, 1\u201322 (11 2022). https:\/\/doi.org\/10.1007\/s12065-020-00540-3","DOI":"10.1007\/s12065-020-00540-3"},{"key":"8_CR12","doi-asserted-by":"publisher","unstructured":"Tang, X.: The role of artificial intelligence in medical imaging research. BJR$$|$$Open 2(1), 20190031 (2020). https:\/\/doi.org\/10.1259\/bjro.20190031","DOI":"10.1259\/bjro.20190031"},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"19200","DOI":"10.1038\/s41598-022-21848-3","volume":"12","author":"W Voon","year":"2022","unstructured":"Voon, W., et al.: Performance analysis of seven convolutional neural networks (CNNs) with transfer learning for invasive ductal carcinoma (IDC) grading in breast histopathological images. Sci. Rep. 12, 19200 (2022). https:\/\/doi.org\/10.1038\/s41598-022-21848-3","journal-title":"Sci. Rep."},{"key":"8_CR14","doi-asserted-by":"publisher","unstructured":"Wakili, M.A., et al.: Classification of breast cancer histopathological images using DenseNet and transfer learning. Comput. Intell. Neurosci. 2022 (2022). https:\/\/doi.org\/10.1155\/2022\/8904768","DOI":"10.1155\/2022\/8904768"},{"key":"8_CR15","unstructured":"Wild, C., Weiderpass, E., Stewart, B. (eds.): World Cancer Report: Cancer Research for Cancer Prevention. International Agency for Research on Cancer, Lyon (2020)"},{"key":"8_CR16","doi-asserted-by":"publisher","first-page":"80","DOI":"10.3389\/fgene.2019.00080","volume":"10","author":"J Xie","year":"2019","unstructured":"Xie, J., Liu, R., Luttrell, J., Zhang, C.: Deep learning based analysis of histopathological images of breast cancer. Front. Genet. 10, 80 (2019)","journal-title":"Front. Genet."},{"key":"8_CR17","doi-asserted-by":"publisher","unstructured":"Yun, J., Chen, L., Zhang, H., Xiao, X.: Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PLoS ONE 14 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0214587","DOI":"10.1371\/journal.pone.0214587"},{"key":"8_CR18","doi-asserted-by":"publisher","unstructured":"Zeiser, F.A., da Costa, C.A., Roehe, A.V., da Rosa Righi, R., Marques, N.M.C.: Breast cancer intelligent analysis of histopathological data: a systematic review. Appl. Soft Comput. 113, 107886 (2021). https:\/\/doi.org\/10.1016\/j.asoc.2021.107886, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1568494621008085","DOI":"10.1016\/j.asoc.2021.107886"}],"container-title":["Lecture Notes in Networks and Systems","Novel &amp; Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44097-7_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T09:08:31Z","timestamp":1695373711000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44097-7_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031440960","9783031440977"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44097-7_8","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"23 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NiDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Novel & Intelligent Digital Systems Conferences","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nids2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iis-international.org\/nids2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}