{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T01:42:29Z","timestamp":1748569349651,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031264375"},{"type":"electronic","value":"9783031264382"}],"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:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":53,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Mammography is a popular diagnostic imaging procedure for detecting breast cancer at an early stage. Various deep-learning approaches to breast cancer detection incur high costs and are erroneous. Therefore, they are not reliable to be used by medical practitioners. Specifically, these approaches do not exploit complex texture patterns and interactions. These approaches warrant the need for labelled data to enable learning, limiting the scalability of these methods with insufficient labelled datasets. Further, these models lack generalisation capability to new-synthesised patterns\/textures. To address these problems, in the first instance, we design a graph model to transform the mammogram images into a highly correlated multigraph that encodes rich structural relations and high-level texture features. Next, we integrate a pre-training self-supervised learning multigraph encoder (SSL-MG) to improve feature presentations, especially under limited labelled data constraints. Then, we design a semi-supervised mammogram multigraph convolution neural network downstream model (MMGCN) to perform multi-classifications of mammogram segments encoded in the multigraph nodes. Our proposed frameworks, SSL-MGCN and MMGCN, reduce the need for annotated data to 40% and 60%, respectively, in contrast to the conventional methods that require more than 80% of data to be labelled. Finally, we evaluate the classification performance of MMGCN independently and with integration with SSL-MG in a model called SSL-MMGCN over multi-training settings. Our evaluation results on DSSM, one of the recent public datasets, demonstrate the efficient learning performance of SSL-MNGCN and MMGCN with 0.97 and 0.98 AUC classification accuracy in contrast to the multitask deep graph (GCN) method Hao Du et al. (2021) with 0.81 AUC accuracy.<\/jats:p>","DOI":"10.1007\/978-3-031-26438-2_4","type":"book-chapter","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T06:32:56Z","timestamp":1677047576000},"page":"40-54","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-Graph Convolutional Neural Network for\u00a0Breast Cancer Multi-task Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5622-9854","authenticated-orcid":false,"given":"Mohamed","family":"Ibrahim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8753-5467","authenticated-orcid":false,"given":"Shagufta","family":"Henna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9009-6138","authenticated-orcid":false,"given":"Gary","family":"Cullen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"issue":"7","key":"4_CR1","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1158\/1055-9965.EPI-07-0152","volume":"16","author":"V Bataille","year":"2007","unstructured":"Bataille, V., et al.: Nevus size and number are associated with telomere length and represent potential markers of a decreased senescence in vivo. Cancer Epidemiol. Prev. Biomark. 16(7), 1499\u20131502 (2007)","journal-title":"Cancer Epidemiol. Prev. Biomark."},{"key":"4_CR2","doi-asserted-by":"crossref","unstructured":"K\u00f6sters, J.P., G\u00f8tzsche, P.C.: Regular self-examination or clinical examination for early detection of breast cancer. Cochrane Database Syst. Rev. (2) (2003)","DOI":"10.1002\/14651858.CD003373"},{"issue":"2","key":"4_CR3","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/s10549-017-4527-7","volume":"167","author":"JJ Mordang","year":"2018","unstructured":"Mordang, J.J., et al.: The importance of early detection of calcifications associated with breast cancer in screening. Breast Cancer Res. Treat. 167(2), 451\u2013458 (2018)","journal-title":"Breast Cancer Res. Treat."},{"issue":"5","key":"4_CR4","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1258\/ar.2011.100357","volume":"52","author":"S Hofvind","year":"2011","unstructured":"Hofvind, S., Iversen, B.F., Eriksen, L., Styr, B.M., Kjellevold, K., Kurz, K.D.: Mammographic morphology and distribution of calcifications in ductal carcinoma in situ diagnosed in organized screening. Acta Radiol. 52(5), 481\u2013487 (2011)","journal-title":"Acta Radiol."},{"issue":"4","key":"4_CR5","doi-asserted-by":"publisher","first-page":"282","DOI":"10.4103\/0971-3026.57208","volume":"19","author":"YV Nalawade","year":"2009","unstructured":"Nalawade, Y.V.: Evaluation of breast calcifications. Indian J. Radiol. Imaging 19(4), 282\u2013286 (2009)","journal-title":"Indian J. Radiol. Imaging"},{"issue":"22","key":"4_CR6","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","volume":"318","author":"BE Bejnordi","year":"2017","unstructured":"Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199\u20132210 (2017)","journal-title":"JAMA"},{"issue":"1","key":"4_CR7","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1177\/0284185118770917","volume":"60","author":"EL Henriksen","year":"2019","unstructured":"Henriksen, E.L., Carlsen, J.F., Vejborg, I.M., Nielsen, M.B., Lauridsen, C.A.: The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review. Acta Radiologica 60(1), 13\u201318 (2019)","journal-title":"Acta Radiologica"},{"issue":"1","key":"4_CR8","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1038\/sj.bjc.6603506","volume":"96","author":"A Katalinic","year":"2007","unstructured":"Katalinic, A., Bartel, C., Raspe, H., Schreer, I.: Beyond mammography screening: quality assurance in breast cancer diagnosis (The QuaMaDi Project). Br. J. Cancer 96(1), 157\u2013161 (2007)","journal-title":"Br. J. Cancer"},{"issue":"11","key":"4_CR9","first-page":"1","volume":"20","author":"D Abdelhafiz","year":"2019","unstructured":"Abdelhafiz, D., Yang, C., Ammar, R., Nabavi, S.: Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinform. 20(11), 1\u201320 (2019)","journal-title":"BMC Bioinform."},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Guan, S., Loew, M.: Breast cancer detection using transfer learning in convolutional neural networks. In: Conference on AIPR 2017 IEEE Applied Imagery Pattern Recognition Workshop, pp. 1\u20138 (2017)","DOI":"10.1109\/AIPR.2017.8457948"},{"key":"4_CR11","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint (2014). arXiv:1409.1556"},{"issue":"1","key":"4_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-48995-4","volume":"9","author":"L Shen","year":"2019","unstructured":"Shen, L., Margolies, L.R., Rothstein, J.H., Fluder, E., McBride, R., Sieh, W.: Deep learning to improve breast cancer detection on screening mammography. Sci. Rep. 9(1), 1\u201312 (2019)","journal-title":"Sci. Rep."},{"key":"4_CR13","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, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5","key":"4_CR14","doi-asserted-by":"publisher","first-page":"4701","DOI":"10.1016\/j.aej.2021.03.048","volume":"60","author":"WM Salama","year":"2021","unstructured":"Salama, W.M., Wessam, M., Aly, M.H.: Deep learning in mammography images segmentation and classification: automated CNN approach. Alex. Eng. J. 60(5), 4701\u20134709 (2021)","journal-title":"Alex. Eng. J."},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Ballester, P., Araujo, R.M.: On the performance of GoogLeNet and AlexNet applied to sketches. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)","DOI":"10.1609\/aaai.v30i1.10171"},{"key":"4_CR16","unstructured":"Alom, M.Z., et al.: The history began from alexnet: a comprehensive survey on deep learning approaches. arXiv preprint (2018). arXiv:1803.01164"},{"key":"4_CR17","unstructured":"Du, H., Yao, M.M.S., Chen, L., Chan, W.P., Feng, M.: Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms. arXiv preprint (2021). arXiv:2105.06822"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lee, W.S.: Deep graphical feature learning for the feature matching problem. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5087\u20135096 (2019)","DOI":"10.1109\/ICCV.2019.00519"},{"key":"4_CR19","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.media.2018.10.011","volume":"51","author":"C Gallego-Ortiz","year":"2019","unstructured":"Gallego-Ortiz, C., Martel, A.L.: A graph-based lesion characterization and deep embedding approach for improved computer-aided diagnosis of nonmass breast MRI lesions. Med. Image Anal. 51, 116\u2013124 (2019)","journal-title":"Med. Image Anal."},{"key":"4_CR20","unstructured":"Kipf, T.N., Thomas, N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint (2016). arXiv:1609.02907"},{"key":"4_CR21","unstructured":"Du, H., Feng, J., Feng, M.: Zoom in to where it matters: a hierarchical graph based model for mammogram analysis. arXiv preprint (2019). arXiv:1912.07517"},{"issue":"2","key":"4_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102439","volume":"58","author":"YD Zhang","year":"2021","unstructured":"Zhang, Y.D., Satapathy, S.C., Guttery, D.S., G\u00f3rriz, J.M., Wang, S.H.: Improved breast cancer classification through combining graph convolutional network and convolutional neural network. Inf. Process. Manag. 58(2), 102439 (2021)","journal-title":"Inf. Process. Manag."},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, R.J., Lu, M.Y., Baras, A., Mahmood, F.: Weakly supervised prostate TMA classification via graph convolutional networks. In: Conference on ISBI 2020 IEEE 17th International Symposium on Biomedical Imaging, pp. 239\u2013243 (2020)","DOI":"10.1109\/ISBI45749.2020.9098534"},{"key":"4_CR24","unstructured":"\u00d6zen, Y.: Self-supervised representation learning with graph neural networks for region of interest analysis in breast histopathology. Doctoral dissertation, Bilkent University (2020)"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Ma, J., Li, X., Li, H., Wang, R., Menze, B., Zheng, W.S.: Cross-view relation networks for mammogram mass detection. In: Conference on ICPR 2020 25th International Conference on Pattern Recognition, pp. 8632\u20138638 (2021)","DOI":"10.1109\/ICPR48806.2021.9413132"},{"key":"4_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102204","volume":"73","author":"Z Yang","year":"2021","unstructured":"Yang, Z., et al.: MommiNet-v2: mammographic multi-view mass identification networks. Med. Image Anal. 73, 102204 (2021)","journal-title":"Med. Image Anal."},{"key":"4_CR27","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"4_CR28","unstructured":"Dehak, N., Dehak, R., Glass, J.R., Reynolds, D.A., Kenny, P.: Cosine similarity scoring without score normalization techniques. In: Odyssey, p. 15 (2010)"},{"key":"4_CR29","unstructured":"Mondal, A.K., Jain, V., Siddiqi, K.: Mini-batch graphs for robust image classification. arXiv preprint (2021). arXiv:2105.03237"},{"key":"4_CR30","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)"},{"issue":"1","key":"4_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.177","volume":"4","author":"RS Lee","year":"2017","unstructured":"Lee, R.S., Gimenez, F., Hoogi, A., Miyake, K.K., Gorovoy, M., Rubin, D.L.: A curated mammography data set for use in computer-aided detection and diagnosis research. Sci. Data 4(1), 1\u20139 (2017)","journal-title":"Sci. Data"},{"key":"4_CR32","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint (2016). arXiv:1609.02907"},{"issue":"1","key":"4_CR33","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/TMI.2021.3102622","volume":"41","author":"H Li","year":"2021","unstructured":"Li, H., Chen, D., Nailon, W.H., Davies, M.E., Laurenson, D.I.: Dual convolutional neural networks for breast mass segmentation and diagnosis in mammography. IEEE Trans. Med. Imaging 41(1), 3\u201313 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR34","unstructured":"Du, H., Yao, M.M.S., Chen, L., Chan, W.P., Feng, M.: Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms. arXiv preprint, vol. 14 (2021). arXiv:2105.06822"},{"key":"4_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1007\/978-3-319-46723-8_13","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"N Dhungel","year":"2016","unstructured":"Dhungel, N., Carneiro, G., Bradley, A.P.: The automated learning of deep features for breast mass classification from mammograms. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 106\u2013114. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_13"},{"key":"4_CR36","doi-asserted-by":"crossref","unstructured":"Al-Antari, M.A., Al-Masni, M.A., Kim, T.S.: Deep learning computer-aided diagnosis for breast lesion in digital mammogram. Deep Learn. Med. Image Anal. 59\u201372 (2020)","DOI":"10.1007\/978-3-030-33128-3_4"},{"key":"4_CR37","unstructured":"Le, T.L.T., Thome, N., Bernard, S., Bismuth, V., Patoureaux, F.: Multitask classification and segmentation for cancer diagnosis in mammography. arXiv preprint (2019). arXiv:1909.05397"}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence and Cognitive Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26438-2_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T06:34:12Z","timestamp":1677047652000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26438-2_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031264375","9783031264382"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26438-2_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"23 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Irish Conference on Artificial Intelligence and Cognitive Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munster","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","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":"8 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aics2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aics2022.mtu.ie\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"102","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":"41","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":"40% - 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":"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)"}}]}}