{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:33:13Z","timestamp":1743049993418,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030617011"},{"type":"electronic","value":"9783030617028"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-61702-8_29","type":"book-chapter","created":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T23:02:31Z","timestamp":1603062151000},"page":"420-432","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Tissue Differentiation Based on Classification of Morphometric Features of Nuclei"],"prefix":"10.1007","author":[{"given":"Dominika","family":"Dudzi\u0144ska","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4773-5322","authenticated-orcid":false,"given":"Adam","family":"Pi\u00f3rkowski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"key":"29_CR1","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, T.: Classification of breast cancer histology images using convolutional neural networks. PLoS ONE 12(6), e0177544 (2017)","DOI":"10.1371\/journal.pone.0177544"},{"issue":"2","key":"29_CR2","doi-asserted-by":"crossref","first-page":"e260","DOI":"10.1002\/wdev.260","volume":"6","author":"ET Arena","year":"2017","unstructured":"Arena, E.T., Rueden, C.T., Hiner, M.C., Wang, S., Yuan, M., Eliceiri, K.W.: Quantitating the cell: turning images into numbers with ImageJ. Wiley Interdiscipl. Rev. Dev. Biol. 6(2), e260 (2017)","journal-title":"Wiley Interdiscipl. Rev. Dev. Biol."},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Chang, H., Borowsky, A., Spellman, P., Parvin, B.: Classification of tumor histology via morphometric context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2203\u20132210 (2013)","DOI":"10.1109\/CVPR.2013.286"},{"issue":"6","key":"29_CR4","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045\u20131057 (2013)","journal-title":"J. Digit. Imaging"},{"issue":"S1","key":"29_CR5","doi-asserted-by":"crossref","first-page":"S25","DOI":"10.2144\/000112517","volume":"43","author":"TJ Collins","year":"2007","unstructured":"Collins, T.J.: ImageJ for microscopy. Biotechniques 43(S1), S25\u2013S30 (2007)","journal-title":"Biotechniques"},{"key":"29_CR6","unstructured":"Demir, C., Yener, B.: Automated cancer diagnosis based on histopathological images: a systematic survey. Technical report, Rensselaer Polytechnic Institute (2005)"},{"issue":"1","key":"29_CR7","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s00138-010-0275-y","volume":"23","author":"JW Han","year":"2012","unstructured":"Han, J.W., Breckon, T.P., Randell, D.A., Landini, G.: The application of support vector machine classification to detect cell nuclei for automated microscopy. Mach. Vis. Appl. 23(1), 15\u201324 (2012)","journal-title":"Mach. Vis. Appl."},{"key":"29_CR8","doi-asserted-by":"publisher","DOI":"10.7937\/tcia.2019.4a4dkp9u","author":"L Hou","year":"2019","unstructured":"Hou, L., et al.: Dataset of segmented nuclei in hematoxylin and eosin stained histopathology images of 10 cancer types. Can. Imaging Arch. (2019). \nhttps:\/\/doi.org\/10.7937\/tcia.2019.4a4dkp9u","journal-title":"Can. Imaging Arch."},{"issue":"4","key":"29_CR9","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1016\/j.patcog.2009.10.014","volume":"43","author":"PW Huang","year":"2010","unstructured":"Huang, P.W., Lai, Y.H.: Effective segmentation and classification for HCC biopsy images. Pattern Recogn. 43(4), 1550\u20131563 (2010)","journal-title":"Pattern Recogn."},{"key":"29_CR10","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/RBME.2013.2295804","volume":"7","author":"H Irshad","year":"2013","unstructured":"Irshad, H., Veillard, A., Roux, L., Racoceanu, D.: Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential. IEEE Rev. Biomed. Eng. 7, 97\u2013114 (2013)","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"29_CR11","doi-asserted-by":"publisher","unstructured":"Jaworek-Korjakowska, J., K\u0142eczek, P.: Automatic classification of specific melanocytic lesions using artificial intelligence. BioMed Res. Int. (2016). article ID 8934242 \nhttps:\/\/doi.org\/10.1155\/2016\/8934242","DOI":"10.1155\/2016\/8934242"},{"key":"29_CR12","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1007\/978-3-540-68825-9_14","volume-title":"Advances in Artificial Intelligence","author":"\u0141 Jele\u0144","year":"2008","unstructured":"Jele\u0144, \u0141., Krzy\u017cak, A., Fevens, T.: Comparison of pleomorphic and structural features used for breast cancer malignancy classification. In: Bergler, S. (ed.) AI 2008. LNCS (LNAI), vol. 5032, pp. 138\u2013149. Springer, Heidelberg (2008). \nhttps:\/\/doi.org\/10.1007\/978-3-540-68825-9_14"},{"issue":"1","key":"29_CR13","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1186\/s12967-019-1839-x","volume":"17","author":"MY Ji","year":"2019","unstructured":"Ji, M.Y., et al.: Nuclear shape, architecture and orientation features from h&e images are able to predict recurrence in node-negative gastric adenocarcinoma. J. Transl. Med. 17(1), 92 (2019)","journal-title":"J. Transl. Med."},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Kalinin, A.A., et al.: 3D cell nuclear morphology: microscopy imaging dataset and voxel-based morphometry classification results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2272\u20132280 (2018)","DOI":"10.1109\/CVPRW.2018.00304"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Kaucha, D.P., Prasad, P., Alsadoon, A., Elchouemi, A., Sreedharan, S.: Early detection of lung cancer using SVM classifier in biomedical image processing. In: 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 3143\u20133148. IEEE (2017)","DOI":"10.1109\/ICPCSI.2017.8392305"},{"issue":"4","key":"29_CR16","doi-asserted-by":"crossref","first-page":"759","DOI":"10.2478\/amcs-2018-0058","volume":"28","author":"M Kowal","year":"2018","unstructured":"Kowal, M., Skobel, M., Nowicki, N.: The feature selection problem in computer-assisted cytology. Int. J. Appl. Math. Comput. Sci. 28(4), 759\u2013770 (2018)","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"29_CR17","first-page":"2579","volume":"9","author":"LVD Maaten","year":"2008","unstructured":"Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"29_CR18","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s11517-018-1819-y","volume":"57","author":"S Pang","year":"2019","unstructured":"Pang, S., Du, A., Orgun, M.A., Yu, Z.: A novel fused convolutional neural network for biomedical image classification. Med. Biol. Eng. Comput. 57(1), 107\u2013121 (2019)","journal-title":"Med. Biol. Eng. Comput."},{"issue":"7","key":"29_CR19","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1038\/nmeth.2089","volume":"9","author":"CA Schneider","year":"2012","unstructured":"Schneider, C.A., Rasband, W.S., Eliceiri, K.W.: NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671\u2013675 (2012)","journal-title":"Nat. Methods"},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Swiderska-Chadaj, Z., et al.: A deep learning approach to assess the predominant tumor growth pattern in whole-slide images of lung adenocarcinoma. In: Medical Imaging 2020: Digital Pathology, vol. 11320, p. 113200D. International Society for Optics and Photonics (2020)","DOI":"10.1117\/12.2549742"},{"issue":"4","key":"29_CR21","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.prp.2012.02.007","volume":"208","author":"M Vertemati","year":"2012","unstructured":"Vertemati, M., et al.: Morphometric analysis of hepatocellular nodular lesions in HCV cirrhosis. Pathol. Res. Pract. 208(4), 240\u2013244 (2012)","journal-title":"Pathol. Res. Pract."},{"key":"29_CR22","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/srep00503","volume":"2","author":"S Wienert","year":"2012","unstructured":"Wienert, S., et al.: Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach. Sci. Rep. 2, 503 (2012)","journal-title":"Sci. Rep."},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Wu, B., Nebylitsa, S.V., Mukherjee, S., Jain, M.: Quantitative diagnosis of bladder cancer by morphometric analysis of HE images. In: Photonic Therapeutics and Diagnostics XI, vol. 9303, p. 930317. International Society for Optics and Photonics (2015)","DOI":"10.1117\/12.2083559"},{"issue":"6","key":"29_CR24","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1007\/s00428-016-1923-z","volume":"468","author":"Y Yamashita","year":"2016","unstructured":"Yamashita, Y., Ichihara, S., Moritani, S., Yoon, H.S., Yamaguchi, M.: Does flat epithelial atypia have rounder nuclei than columnar cell change\/hyperplasia? a morphometric approach to columnar cell lesions of the breast. Virchows Arch. 468(6), 663\u2013673 (2016)","journal-title":"Virchows Arch."}],"container-title":["Communications in Computer and Information Science","Applied Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61702-8_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T23:15:48Z","timestamp":1603062948000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-61702-8_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030617011","9783030617028"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61702-8_29","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Informatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ota","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nigeria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icai22020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icai.itiud.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":"101","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":"35","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":"35% - 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":"4","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":"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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}