{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T13:14:36Z","timestamp":1763644476092,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031781032"},{"type":"electronic","value":"9783031781049"}],"license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-78104-9_6","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T21:44:26Z","timestamp":1733089466000},"page":"76-91","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Integrated Grading Framework for\u00a0Histopathological Breast Cancer: Multi-level Vision Transformers, Textural Features, and\u00a0Fusion Probability Network"],"prefix":"10.1007","author":[{"given":"Hossam Magdy","family":"Balaha","sequence":"first","affiliation":[]},{"given":"Khadiga M.","family":"Ali","sequence":"additional","affiliation":[]},{"given":"Ali","family":"Mahmoud","sequence":"additional","affiliation":[]},{"given":"Mohammed","family":"Ghazal","sequence":"additional","affiliation":[]},{"given":"Ayman","family":"El-Baz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"issue":"3","key":"6_CR1","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/s00521-018-3882-6","volume":"32","author":"MK Abd Ghani","year":"2020","unstructured":"Abd Ghani, M.K., et al.: Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques. Neural Comput. Appl. 32(3), 625\u2013638 (2020). https:\/\/doi.org\/10.1007\/s00521-018-3882-6","journal-title":"Neural Comput. Appl."},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Aboudessouki, A., et al.: Automated diagnosis of breast cancer using deep learning-based whole slide image analysis of molecular biomarkers. In: 2023 IEEE International Conference on Image Processing (ICIP), pp. 2965\u20132969 (2023). https:\/\/doi.org\/10.1109\/ICIP49359.2023.10222479","DOI":"10.1109\/ICIP49359.2023.10222479"},{"key":"6_CR3","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/978-981-16-2641-8_8","volume-title":"Data Engineering for Smart Systems: Proceedings of SSIC 2021","author":"P Agarwal","year":"2022","unstructured":"Agarwal, P., Yadav, A., Mathur, P.: Breast cancer prediction on BreakHis dataset using deep CNN and transfer learning model. In: Nanda, P., Verma, V.K., Srivastava, S., Gupta, R.K., Mazumdar, A.P. (eds.) Data Engineering for Smart Systems: Proceedings of SSIC 2021, pp. 77\u201388. Springer Singapore, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-2641-8_8"},{"key":"6_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.iswa.2022.200066","volume":"14","author":"A Ameh Joseph","year":"2022","unstructured":"Ameh Joseph, A., Abdullahi, M., Junaidu, S.B., Hassan Ibrahim, H., Chiroma, H.: Improved multi-classification of breast cancer histopathological images using handcrafted features and deep neural network (dense layer). Intell. Syst. Appl. 14, 200066 (2022). https:\/\/doi.org\/10.1016\/j.iswa.2022.200066","journal-title":"Intell. Syst. Appl."},{"key":"6_CR5","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1054","volume":"8","author":"NA Baghdadi","year":"2022","unstructured":"Baghdadi, N.A., Malki, A., Balaha, H.M., AbdulAzeem, Y., Badawy, M., Elhosseini, M.: Classification of breast cancer using a manta-ray foraging optimized transfer learning framework. PeerJ Comput. Sci. 8, e1054 (2022)","journal-title":"PeerJ Comput. Sci."},{"issue":"6","key":"6_CR6","doi-asserted-by":"publisher","first-page":"7897","DOI":"10.1007\/s12652-023-04600-1","volume":"14","author":"HM Balaha","year":"2023","unstructured":"Balaha, H.M., Antar, E.R., Saafan, M.M., El-Gendy, E.M.: A comprehensive framework towards segmenting and classifying breast cancer patients using deep learning and Aquila optimizer. J. Ambient. Intell. Humaniz. Comput. 14(6), 7897\u20137917 (2023). https:\/\/doi.org\/10.1007\/s12652-023-04600-1","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"issue":"12","key":"6_CR7","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.3390\/cancers16122222","volume":"16","author":"AA Balasubramanian","year":"2024","unstructured":"Balasubramanian, A.A., et al.: Ensemble deep learning-based image classification for breast cancer subtype and invasiveness diagnosis from whole slide image histopathology. Cancers 16(12), 2222 (2024)","journal-title":"Cancers"},{"key":"6_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2021.110055","volume":"146","author":"G Corrias","year":"2022","unstructured":"Corrias, G., Micheletti, G., Barberini, L., Suri, J.S., Saba, L.: Texture analysis imaging \u201cwhat a clinical radiologist needs to know\u2019\u2019. Eur. J. Radiol. 146, 110055 (2022). https:\/\/doi.org\/10.1016\/j.ejrad.2021.110055","journal-title":"Eur. J. Radiol."},{"key":"6_CR9","unstructured":"Dosovitskiy, A., et al.: An image is worth 16$$\\,\\times \\,$$16 words: transformers for image recognition at scale (2020)"},{"key":"6_CR10","doi-asserted-by":"publisher","first-page":"104983","DOI":"10.1109\/access.2024.3432459","volume":"12","author":"A Gamal","year":"2024","unstructured":"Gamal, A., et al.: A novel machine learning approach for predicting neoadjuvant chemotherapy response in breast cancer: integration of multimodal radiomics with clinical and molecular subtype markers. IEEE Access 12, 104983\u2013105003 (2024). https:\/\/doi.org\/10.1109\/access.2024.3432459","journal-title":"IEEE Access"},{"issue":"14","key":"6_CR11","doi-asserted-by":"publisher","first-page":"3608","DOI":"10.3390\/cancers15143608","volume":"15","author":"X Jiang","year":"2023","unstructured":"Jiang, X., Hu, Z., Wang, S., Zhang, Y.: Deep learning for medical image-based cancer diagnosis. Cancers 15(14), 3608 (2023). https:\/\/doi.org\/10.3390\/cancers15143608","journal-title":"Cancers"},{"issue":"5","key":"6_CR12","doi-asserted-by":"publisher","first-page":"838","DOI":"10.3889\/oamjms.2019.171","volume":"7","author":"ZS Lima","year":"2019","unstructured":"Lima, Z.S., Ebadi, M.R., Amjad, G., Younesi, L.: Application of imaging technologies in breast cancer detection: a review article. Open Access Macedonian J. Med. Sci. 7(5), 838\u2013848 (2019). https:\/\/doi.org\/10.3889\/oamjms.2019.171","journal-title":"Open Access Macedonian J. Med. Sci."},{"key":"6_CR13","doi-asserted-by":"publisher","DOI":"10.7937\/TCIA.2019.4YIBTJNO","author":"A Martel","year":"2019","unstructured":"Martel, A., Nofech-Mozes, S., Salama, S., Akbar, S., Peikari, M.: Assessment of residual breast cancer cellularity after neoadjuvant chemotherapy using digital. Pathology (2019). https:\/\/doi.org\/10.7937\/TCIA.2019.4YIBTJNO","journal-title":"Pathology"},{"key":"6_CR14","doi-asserted-by":"publisher","unstructured":"Michael, E., Ma, H., Li, H., Qi, S.: An optimized framework for breast cancer classification using machine learning. BioMed Res. Int. 2022, 8482022 (2022). https:\/\/doi.org\/10.1155\/2022\/8482022","DOI":"10.1155\/2022\/8482022"},{"key":"6_CR15","doi-asserted-by":"publisher","unstructured":"Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3523\u20133542 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3059968","DOI":"10.1109\/TPAMI.2021.3059968"},{"issue":"3","key":"6_CR16","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1007\/s10462-019-09716-5","volume":"53","author":"G Murtaza","year":"2020","unstructured":"Murtaza, G., et al.: Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges. Artif. Intell. Rev. 53(3), 1655\u20131720 (2020). https:\/\/doi.org\/10.1007\/s10462-019-09716-5","journal-title":"Artif. Intell. Rev."},{"issue":"12","key":"6_CR17","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0244965","volume":"15","author":"LO Osapoetra","year":"2020","unstructured":"Osapoetra, L.O., Chan, W., Tran, W., Kolios, M.C., Czarnota, G.J.: Comparison of methods for texture analysis of QUS parametric images in the characterization of breast lesions. PLoS ONE 15(12), e0244965 (2020). https:\/\/doi.org\/10.1371\/journal.pone.0244965","journal-title":"PLoS ONE"},{"key":"6_CR18","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1613\/jair.1.13188","volume":"73","author":"Y Ozaki","year":"2022","unstructured":"Ozaki, Y., Tanigaki, Y., Watanabe, S., Nomura, M., Onishi, M.: Multiobjective tree-structured parzen estimator. J. Artif. Intell. Res. 73, 1209\u20131250 (2022)","journal-title":"J. Artif. Intell. Res."},{"key":"6_CR19","doi-asserted-by":"publisher","unstructured":"Peikari, M., Salama, S., Nofech-Mozes, S., Martel, A.L.: Automatic cellularity assessment from post-treated breast surgical specimens. Cytometry. Part A: J. Int. Soc. Anal. Cytol. 91(11), 1078\u20131087 (2017). https:\/\/doi.org\/10.1002\/cyto.a.23244","DOI":"10.1002\/cyto.a.23244"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Sabry, M., et al.: A vision transformer approach for breast cancer classification in histopathology. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp.\u00a01\u20134. IEEE (2024)","DOI":"10.1109\/ISBI56570.2024.10635515"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Saleh, G.A., et al.: Impact of imaging biomarkers and AI on breast cancer management: a brief review. Cancers 15(21), 5216 (2023)","DOI":"10.3390\/cancers15215216"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Seo, H., Brand, L., Barco, L.S., Wang, H.: Scaling multi-instance support vector machine to breast cancer detection on the BreaKHis dataset. Bioinformatics 38(Supplement_1), i92\u2013i100 (2022)","DOI":"10.1093\/bioinformatics\/btac267"},{"issue":"3","key":"6_CR23","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1007\/s10278-019-00307-y","volume":"33","author":"S Sharma","year":"2020","unstructured":"Sharma, S., Mehra, R.: Conventional machine learning and deep learning approach for multi-classification of breast cancer histopathology images-a comparative insight. J. Digit. Imaging 33(3), 632\u2013654 (2020). https:\/\/doi.org\/10.1007\/s10278-019-00307-y","journal-title":"J. Digit. Imaging"},{"key":"6_CR24","unstructured":"Spanhol, F., Oliveira, L., Petitjean, C., Heutte, L.: Breast cancer histopathological database (breakhis) (2021)"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Taheri, S., Golrizkhatami, Z., Basabrain, A.A., Hazzazi, M.S.: A comprehensive study on classification of breast cancer histopathological images: binary versus multi-category and magnification-specific versus magnification-independent. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3386355"},{"issue":"1","key":"6_CR26","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.annonc.2021.09.007","volume":"33","author":"Y Wang","year":"2022","unstructured":"Wang, Y., et al.: Improved breast cancer histological grading using deep learning. Ann. Oncol. 33(1), 89\u201398 (2022). https:\/\/doi.org\/10.1016\/j.annonc.2021.09.007","journal-title":"Ann. Oncol."},{"key":"6_CR27","doi-asserted-by":"publisher","unstructured":"Weigert, M., Schmidt, U.: Nuclei instance segmentation and classification in histopathology images with Stardist. In: 2022 IEEE International Symposium on Biomedical Imaging Challenges (ISBIC), pp.\u00a01\u20134 (2022). https:\/\/doi.org\/10.1109\/ISBIC56247.2022.9854534","DOI":"10.1109\/ISBIC56247.2022.9854534"},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"Wetstein, S.C., et al.: Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images. Sci. Rep. 12(1), 15102 (2022). https:\/\/doi.org\/10.1038\/41598-022-19112-9, publisher: Nature Publishing Group","DOI":"10.1038\/s41598-022-19112-9"},{"issue":"1130","key":"6_CR29","doi-asserted-by":"publisher","first-page":"20211033","DOI":"10.1259\/bjr.20211033","volume":"95","author":"L Wilkinson","year":"2022","unstructured":"Wilkinson, L., Gathani, T.: Understanding breast cancer as a global health concern. Br. J. Radiol. 95(1130), 20211033 (2022). https:\/\/doi.org\/10.1259\/bjr.20211033","journal-title":"Br. J. Radiol."},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Xiao, M., Li, Y., Yan, X., Gao, M., Wang, W.: Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example. In: Proceedings of the 2024 7th International Conference on Machine Vision and Applications, pp. 145\u2013149 (2024)","DOI":"10.1145\/3653946.3653968"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Xu, H., et al.: Vision transformers for computational histopathology. IEEE Rev. Biomedical Eng. (2023)","DOI":"10.1109\/RBME.2023.3297604"},{"key":"6_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.103300","volume":"87","author":"W Zhao","year":"2020","unstructured":"Zhao, W., Zhang, Z., Wang, L.: Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng. Appl. Artif. Intell. 87, 103300 (2020). https:\/\/doi.org\/10.1016\/j.engappai.2019.103300","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1","key":"6_CR33","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1002\/ima.22628","volume":"32","author":"Y Zou","year":"2022","unstructured":"Zou, Y., Zhang, J., Huang, S., Liu, B.: Breast cancer histopathological image classification using attention high-order deep network. Int. J. Imaging Syst. Technol. 32(1), 266\u2013279 (2022)","journal-title":"Int. J. Imaging Syst. Technol."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78104-9_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T23:28:03Z","timestamp":1733095683000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78104-9_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"ISBN":["9783031781032","9783031781049"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78104-9_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}