{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:42:48Z","timestamp":1743100968466,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031733598"},{"type":"electronic","value":"9783031733604"}],"license":[{"start":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T00:00:00Z","timestamp":1728086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T00:00:00Z","timestamp":1728086400000},"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\/978-3-031-73360-4_3","type":"book-chapter","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T15:02:10Z","timestamp":1728054130000},"page":"21-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Follicular Lymphoma Grading Based on\u00a03D-DDcGAN and\u00a0Bayesian CNN Using PET-CT Images"],"prefix":"10.1007","author":[{"given":"Lulu","family":"He","sequence":"first","affiliation":[]},{"given":"Chunjun","family":"Qian","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Teng","sequence":"additional","affiliation":[]},{"given":"Chongyang","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Chong","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,5]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.2147\/CMAR.S349193","volume":"14","author":"Y Li","year":"2023","unstructured":"Li, Y., Zhang, Y., Wang, W., et al.: Follicular lymphoma in China: systematic evaluation of follicular lymphoma prognostic models. Cancer Manage. Res. 14, 1385\u20131393 (2023)","journal-title":"Cancer Manage. Res."},{"issue":"2020","key":"3_CR2","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1002\/ajh.25696","volume":"95","author":"A Freedman","year":"2020","unstructured":"Freedman, A., Jacobsen, E.: Follicular lymphoma: update on diagnosis and management. Am. J. Hematol. 95(2020), 316\u2013327 (2020)","journal-title":"Am. J. Hematol."},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1186\/s13045-021-01139-6","volume":"14","author":"J Zha","year":"2021","unstructured":"Zha, J., Fan, L., Yi, S., et al.: Clinical features and outcomes of 1845 patients with follicular lymphoma: a real-world multicenter experience in China. J. Hematol. Oncol. 14, 131 (2021)","journal-title":"J. Hematol. Oncol."},{"issue":"1","key":"3_CR4","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.pathol.2019.09.010","volume":"52","author":"C Randall","year":"2020","unstructured":"Randall, C., Fedoriw, Y.: Pathology and diagnosis of follicular lymphoma and related entities. Pathology 52(1), 30\u201339 (2020)","journal-title":"Pathology"},{"issue":"20","key":"3_CR5","doi-asserted-by":"publisher","first-page":"2375","DOI":"10.1182\/blood-2016-01-643569","volume":"127","author":"SH Swerdlow","year":"2016","unstructured":"Swerdlow, S.H., Campo, E., Pileri, S.A., et al.: The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood 127(20), 2375\u20132390 (2016)","journal-title":"Blood"},{"key":"3_CR6","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1007\/s00259-021-05626-3","volume":"49","author":"FM de Jesus","year":"2022","unstructured":"de Jesus, F.M., Yin, Y., Mantzorou-Kyriaki, E., et al.: Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [18F]FDG PET\/CT features. Eur. J. Nucl. Med. Mol. Imaging 49, 1535\u20131543 (2022)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"3_CR7","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s00330-022-09031-8","volume":"33","author":"C Yuan","year":"2023","unstructured":"Yuan, C., Shi, Q., Huang, X., et al.: Multimodal deep learning model on interim [18F]FDG PET\/CT for predicting primary treatment failure in diffuse large B-cell lymphoma. Eur. Radiol. 33, 77\u201388 (2023)","journal-title":"Eur. Radiol."},{"key":"3_CR8","doi-asserted-by":"publisher","first-page":"175947","DOI":"10.1109\/ACCESS.2019.2955382","volume":"7","author":"Z Yang","year":"2019","unstructured":"Yang, Z., Chen, Y., Le, Z., et al.: Multi-source medical image fusion based on Wasserstein generative adversarial networks. IEEE Access 7, 175947\u2013175958 (2019)","journal-title":"IEEE Access"},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/s10278-022-00696-7","volume":"36","author":"C Fan","year":"2023","unstructured":"Fan, C., Lin, H., Qiu, Y.: U-patch GAN: a medical image fusion method based on GAN. J. Digit. Imaging 36, 339\u2013355 (2023)","journal-title":"J. Digit. Imaging"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Xu, H., Liang, P., Yu, W., et al.: Learning a generative model for fusing infrared and visible images via conditional generative adversarial network with dual discriminators. In: IJCAI, pp. 3954\u20133960 (2019)","DOI":"10.24963\/ijcai.2019\/549"},{"key":"3_CR11","doi-asserted-by":"publisher","first-page":"4980","DOI":"10.1109\/TIP.2020.2977573","volume":"29","author":"J Ma","year":"2020","unstructured":"Ma, J., Xu, H., Jiang, J., et al.: DDcGAN: a dual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Trans. Image Process. 29, 4980\u20134995 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"3_CR12","doi-asserted-by":"publisher","first-page":"55145","DOI":"10.1109\/ACCESS.2020.2982016","volume":"8","author":"J Huang","year":"2020","unstructured":"Huang, J., Le, Z., Ma, Y., et al.: MGMDcGAN: medical image fusion using multi-generator multi-discriminator conditional generative adversarial network. IEEE Access 8, 55145\u201355157 (2020)","journal-title":"IEEE Access"},{"key":"3_CR13","doi-asserted-by":"publisher","first-page":"6595","DOI":"10.1007\/s00521-020-05421-5","volume":"33","author":"C Zhao","year":"2021","unstructured":"Zhao, C., Wang, T., Lei, B.: Medical image fusion method based on dense block and deep convolutional generative adversarial network. Neural Comput. Appl. 33, 6595\u20136610 (2021)","journal-title":"Neural Comput. Appl."},{"key":"3_CR14","doi-asserted-by":"publisher","first-page":"5450373","DOI":"10.1155\/2019\/5450373","volume":"2019","author":"W Tang","year":"2019","unstructured":"Tang, W., Liu, Y., Zhang, C., et al.: Green fluorescent protein and phase-contrast image fusion via generative adversarial networks. Comput. Math. Methods Med. 2019, 5450373 (2019)","journal-title":"Comput. Math. Methods Med."},{"key":"3_CR15","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.inffus.2020.10.015","volume":"67","author":"C Wang","year":"2021","unstructured":"Wang, C., Yang, G., Papanastasiou, G., et al.: DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis. Inf. Fusion 67, 147\u2013160 (2021)","journal-title":"Inf. Fusion"},{"issue":"16","key":"3_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.986153","volume":"9","author":"L Tang","year":"2022","unstructured":"Tang, L., Hui, Y., Yang, H., et al.: Medical image fusion quality assessment based on conditional generative adversarial network. Front. Neurosci. 9(16), 986153 (2022)","journal-title":"Front. Neurosci."},{"issue":"6","key":"3_CR17","doi-asserted-by":"publisher","first-page":"409","DOI":"10.7555\/JBR.36.20220037","volume":"36","author":"HA Amirkolaee","year":"2022","unstructured":"Amirkolaee, H.A., Amirkolaee, H.A.: Medical image translation using an edge-guided generative adversarial network with global-to-local feature fusion. J. Biomed. Res. 36(6), 409\u2013422 (2022)","journal-title":"J. Biomed. Res."},{"key":"3_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102472","volume":"79","author":"P Huang","year":"2022","unstructured":"Huang, P., Li, D., Jiao, Z., et al.: Common feature learning for brain tumor MRI synthesis by context-aware generative adversarial network. Med. Image Anal. 79, 102472 (2022)","journal-title":"Med. Image Anal."},{"key":"3_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106769","volume":"157","author":"X Liu","year":"2023","unstructured":"Liu, X., Chen, H., Yao, C., et al.: BTMF-GAN: a multi-modal MRI fusion generative adversarial network for brain tumors. Comput. Biol. Med. 157, 106769 (2023)","journal-title":"Comput. Biol. Med."},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Vente, C.d., Vos, P., Hosseinzadeh, M., et al.: Deep learning regression for prostate cancer detection and grading in Bi-parametric MRI. IEEE Trans. Biomed. Eng. 68(2), 374\u2013383 (2021)","DOI":"10.1109\/TBME.2020.2993528"},{"issue":"8","key":"3_CR21","doi-asserted-by":"publisher","first-page":"3884","DOI":"10.1109\/JBHI.2022.3179014","volume":"26","author":"M Fan","year":"2022","unstructured":"Fan, M., Yuan, C., Huang, G., et al.: A framework for deep multitask learning with multiparametric magnetic resonance imaging for the joint prediction of histological characteristics in breast cancer. IEEE J. Biomed. Health Inform. 26(8), 3884\u20133895 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"3_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107804","volume":"242","author":"R Sun","year":"2023","unstructured":"Sun, R., Wei, L., Hou, X., et al.: Molecular-subtype guided automatic invasive breast cancer grading using dynamic contrast-enhanced MRI. Comput. Methods Programs Biomed. 242, 107804 (2023)","journal-title":"Comput. Methods Programs Biomed."},{"key":"3_CR23","unstructured":"Shridhar, K., Laumann, F., Liwicki, M.: A comprehensive guide to Bayesian convolutional neural network with variational inference. arXiv preprint arXiv:1901.02731 (2019)"},{"key":"3_CR24","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.inffus.2021.06.001","volume":"76","author":"H Xu","year":"2021","unstructured":"Xu, H., Ma, J.: EMFusion: an unsupervised enhanced medical image fusion network. Inf. Fusion 76, 177\u2013186 (2021)","journal-title":"Inf. Fusion"},{"issue":"7","key":"3_CR25","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.3324\/haematol.2017.181024","volume":"103","author":"H Horn","year":"2018","unstructured":"Horn, H., Kohler, C., Witzig, R., et al.: Gene expression profiling reveals a close relationship between follicular lymphoma grade 3A and 3B, but distinct profiles of follicular lymphoma grade 1 and 2. Haematologica 103(7), 1182\u20131190 (2018)","journal-title":"Haematologica"},{"key":"3_CR26","doi-asserted-by":"publisher","first-page":"3949","DOI":"10.1007\/s00259-023-06405-y","volume":"50","author":"C Jiang","year":"2023","unstructured":"Jiang, C., Qian, C., Jiang, Z., et al.: Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study. Eur. J. Nucl. Med. Mol. Imaging 50, 3949\u20133960 (2023)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"}],"container-title":["Lecture Notes in Computer Science","Computational Mathematics Modeling in Cancer Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73360-4_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T15:02:40Z","timestamp":1728054160000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73360-4_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,5]]},"ISBN":["9783031733598","9783031733604"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73360-4_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,5]]},"assertion":[{"value":"5 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CMMCA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Computational Mathematics Modeling in Cancer Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","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":"6 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2024","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":"cmmca2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cmmcaworkshop.github.io\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}