{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T16:25:53Z","timestamp":1777479953727,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":42,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819784950","type":"print"},{"value":"9789819784967","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"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\/978-981-97-8496-7_13","type":"book-chapter","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T02:02:22Z","timestamp":1730512942000},"page":"178-193","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Modal Learning for Predicting the Progression of Transarterial Chemoembolization Therapy in Hepatocellular Carcinoma"],"prefix":"10.1007","author":[{"given":"Lingzhi","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibo","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinzhu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiachen","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qisen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"issue":"10","key":"13_CR1","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.1007\/s00270-022-03221-z","volume":"45","author":"H Bai","year":"2022","unstructured":"Bai, H., Meng, S., Xiong, C., Liu, Z., Shi, W., Ren, Q., Xia, W., Zhao, X., Jian, J., Song, Y., et al.: Preoperative cect-based radiomic signature for predicting the response of transarterial chemoembolization (tace) therapy in hepatocellular carcinoma. Cardiovasc. Intervent. Radiol. 45(10), 1524\u20131533 (2022)","journal-title":"Cardiovasc. Intervent. Radiol."},{"key":"13_CR2","doi-asserted-by":"publisher","unstructured":"Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68(6), 394\u2013424 (2018). https:\/\/doi.org\/10.3322\/caac.21492","DOI":"10.3322\/caac.21492"},{"issue":"4","key":"13_CR3","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1053\/j.gastro.2015.12.041","volume":"150","author":"J Bruix","year":"2016","unstructured":"Bruix, J., Reig, M., Sherman, M.: Evidence-based diagnosis, staging, and treatment of patients with hepatocellular carcinoma. Gastroenterology 150(4), 835\u2013853 (2016)","journal-title":"Gastroenterology"},{"issue":"5","key":"13_CR4","doi-asserted-by":"publisher","first-page":"S179","DOI":"10.1053\/j.gastro.2004.09.032","volume":"127","author":"J Bruix","year":"2004","unstructured":"Bruix, J., Sala, M., Llovet, J.M.: Chemoembolization for hepatocellular carcinoma. Gastroenterology 127(5), S179\u2013S188 (2004)","journal-title":"Gastroenterology"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Chen, J., Cheung, H.M., Milot, L., Martel, A.L.: Aminn: Autoencoder-based multiple instance neural network improves outcome prediction in multifocal liver metastases, pp. 752\u2013761 (2021)","DOI":"10.1007\/978-3-030-87240-3_72"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Chen, J., et\u00a0al.: Unsupervised clustering of quantitative imaging phenotypes using autoencoder and gaussian mixture model, pp. 575\u2013582 (2019)","DOI":"10.1007\/978-3-030-32251-9_63"},{"issue":"14","key":"13_CR7","doi-asserted-by":"publisher","first-page":"3948","DOI":"10.1158\/1078-0432.CCR-20-4935","volume":"27","author":"NM Cheng","year":"2021","unstructured":"Cheng, N.M., Yao, J., Cai, J., Ye, X., Zhao, S., Zhao, K., Zhou, W., Nogues, I., Huo, Y., Liao, C.T., et al.: Deep learning for fully automated prediction of overall survival in patients with oropharyngeal cancer using fdg-pet imaging. Clin. Cancer Res. 27(14), 3948\u20133959 (2021)","journal-title":"Clin. Cancer Res."},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Fu, S., Lai, H., Li, Q., Liu, Y., Zhang, J., Huang, J., Chen, X., Duan, C., Li, X., Wang, T., et\u00a0al.: Multi-task deep learning network to predict future macrovascular invasion in hepatocellular carcinoma. EClinicalMedicine 42 (2021)","DOI":"10.1016\/j.eclinm.2021.101201"},{"issue":"1","key":"13_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13045-021-01167-2","volume":"14","author":"R Gao","year":"2021","unstructured":"Gao, R., et al.: Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced ct and clinical data. J. Hematology Oncology 14(1), 1\u20137 (2021)","journal-title":"J. Hematology Oncology"},{"issue":"1","key":"13_CR10","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1002\/hep.31022","volume":"72","author":"G Han","year":"2020","unstructured":"Han, G., Berhane, S., Toyoda, H., Bettinger, D., Elshaarawy, O., Chan, A.W., Kirstein, M., Mosconi, C., Hucke, F., Palmer, D., et al.: Prediction of survival among patients receiving transarterial chemoembolization for hepatocellular carcinoma: a response-based approach. Hepatology 72(1), 198\u2013212 (2020)","journal-title":"Hepatology"},{"issue":"1","key":"13_CR11","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","volume":"45","author":"K Han","year":"2022","unstructured":"Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., Tang, Y., Xiao, A., Xu, C., Xu, Y., et al.: A survey on vision transformer. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 87\u2013110 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR12","first-page":"8291","volume":"35","author":"K Han","year":"2022","unstructured":"Han, K., Wang, Y., Guo, J., Tang, Y., Wu, E.: Vision gnn: an image is worth graph of nodes. Adv. Neural. Inf. Process. Syst. 35, 8291\u20138303 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Haywood, N., Gennaro, K., Obert, J., Sauer, P.F., Redden, D.T., Zarzour, J., Smith, J.K., Bolus, D., Saddekni, S., Aal, A.K.A., et\u00a0al.: Does the degree of hepatocellular carcinoma tumor necrosis following transarterial chemoembolization impact patient survival? J. Oncology 2016 (2016)","DOI":"10.1155\/2016\/4692139"},{"key":"13_CR14","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":"2","key":"13_CR15","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.222891","volume":"309","author":"C Hsieh","year":"2023","unstructured":"Hsieh, C., Laguna, A., Ikeda, I., Maxwell, A.W., Chapiro, J., Nadolski, G., Jiao, Z., Bai, H.X.: Using machine learning to predict response to image-guided therapies for hepatocellular carcinoma. Radiology 309(2), e222891 (2023)","journal-title":"Radiology"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"O Kadalayil, L., Benini, R., Pallan, L., O\u2019beirne, J., Marelli, L., Yu, D., Hackshaw, A., Fox, R., Johnson, P., Burroughs, A., et\u00a0al.: A simple prognostic scoring system for patients receiving transarterial embolisation for hepatocellular cancer. Annals Oncology 24(10), 2565\u20132570 (2013)","DOI":"10.1093\/annonc\/mdt247"},{"issue":"1","key":"13_CR17","doi-asserted-by":"publisher","first-page":"67","DOI":"10.3390\/cancers14010067","volume":"14","author":"DS Kim","year":"2021","unstructured":"Kim, D.S., Kim, B.K., Lee, J.S., Lee, H.W., Park, J.Y., Kim, D.Y., Ahn, S.H., Kim, S.U.: Validation of pre-\/post-tace-predict models among patients with hepatocellular carcinoma receiving transarterial chemoembolization. Cancers 14(1), 67 (2021)","journal-title":"Cancers"},{"issue":"3","key":"13_CR18","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1159\/000492535","volume":"7","author":"M Kudo","year":"2018","unstructured":"Kudo, M.: Proposal of primary endpoints for tace combination trials with systemic therapy: lessons learned from 5 negative trials and the positive tactics trial. Liver cancer 7(3), 225\u2013234 (2018)","journal-title":"Liver cancer"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Li, G., Muller, M., Thabet, A., Ghanem, B.: Deepgcns: Can gcns go as deep as cnns? In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00936"},{"issue":"4","key":"13_CR20","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1159\/000505694","volume":"9","author":"F Liu","year":"2020","unstructured":"Liu, F., Liu, D., Wang, K., Xie, X., Su, L., Kuang, M., Huang, G., Peng, B., Wang, Y., Lin, M., et al.: Deep learning radiomics based on contrast-enhanced ultrasound might optimize curative treatments for very-early or early-stage hepatocellular carcinoma patients. Liver Cancer 9(4), 397\u2013413 (2020)","journal-title":"Liver Cancer"},{"key":"13_CR21","unstructured":"Liu, Q.P., Yang, K.L., Xu, X., Liu, X.S., Qu, J.R., Zhang, Y.D.: Radiomics analysis of pretreatment mri in predicting tumor response and outcome in hepatocellular carcinoma with transarterial chemoembolization: a two-center collaborative study. Abdominal Radiology, pp. 1\u201313 (2022)"},{"key":"13_CR22","doi-asserted-by":"publisher","unstructured":"Liu, Z., Sun, Q., Bai, H., Liang, C., Chen, Y., Li, Z.C.: 3d deep attention network for survival prediction from magnetic resonance images in glioblastoma. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1381\u20131384 (2019). https:\/\/doi.org\/10.1109\/ICIP.2019.8803077","DOI":"10.1109\/ICIP.2019.8803077"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Ma, Q.P., He, X.l., Li, K., Wang, J.f., Zeng, Q.J., Xu, E.J., He, X.q., Li, S.y., Kun, W., Zheng, R.Q., et\u00a0al.: Dynamic contrast-enhanced ultrasound radiomics for hepatocellular carcinoma recurrence prediction after thermal ablation. Molecular Imaging Biol. 23, 572\u2013585 (2021)","DOI":"10.1007\/s11307-021-01578-0"},{"key":"13_CR24","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1007\/s00270-017-1606-4","volume":"40","author":"A M\u00e4hringer-Kunz","year":"2017","unstructured":"M\u00e4hringer-Kunz, A., Kloeckner, R., Pitton, M.B., D\u00fcber, C., Schmidtmann, I., Galle, P.R., Koch, S., Weinmann, A.: Validation of the risk prediction models state-score and start-strategy to guide tace treatment in patients with hepatocellular carcinoma. Cardiovasc. Intervent. Radiol. 40, 1017\u20131025 (2017)","journal-title":"Cardiovasc. Intervent. Radiol."},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Morshid, A., Elsayes, K.M., Khalaf, A.M., Elmohr, M.M., Yu, J., Kaseb, A.O., Hassan, M., Mahvash, A., Wang, Z., Hazle, J.D., et\u00a0al.: A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization. Radiol. Artif. Intell. 1(5), e180021 (2019)","DOI":"10.1148\/ryai.2019180021"},{"key":"13_CR26","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.730282","volume":"11","author":"J Peng","year":"2021","unstructured":"Peng, J., Huang, J., Huang, G., Zhang, J.: Predicting the initial treatment response to transarterial chemoembolization in intermediate-stage hepatocellular carcinoma by the integration of radiomics and deep learning. Front. Oncol. 11, 730282 (2021)","journal-title":"Front. Oncol."},{"issue":"6","key":"13_CR27","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.1016\/j.cgh.2014.11.037","volume":"13","author":"DJ Pinato","year":"2015","unstructured":"Pinato, D.J., Arizumi, T., Allara, E., Jang, J.W., Smirne, C., Kim, Y.W., Kudo, M., Pirisi, M., Sharma, R.: Validation of the hepatoma arterial embolization prognostic score in european and asian populations and proposed modification. Clin. Gastroenterol. Hepatol. 13(6), 1204\u20131208 (2015)","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"13_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107882","volume":"243","author":"L Tang","year":"2024","unstructured":"Tang, L., Wang, X., Yang, J., Wang, Y., Qu, M., Li, H.: Dlffnet: a new dynamical local feature fusion network for automatic aortic valve calcification recognition using echocardiography. Comput. Methods Programs Biomed. 243, 107882 (2024)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"3","key":"13_CR29","doi-asserted-by":"publisher","first-page":"1528","DOI":"10.1109\/JBHI.2024.3350247","volume":"28","author":"L Tang","year":"2024","unstructured":"Tang, L., Zhang, Z., Yang, J., Feng, Y., Sun, S., Liu, B., Ma, J., Liu, J., Shao, H.: A new automated prognostic prediction method based on multi-sequence magnetic resonance imaging for hepatic resection of colorectal cancer liver metastases. IEEE J. Biomed. Health Inform. 28(3), 1528\u20131539 (2024). https:\/\/doi.org\/10.1109\/JBHI.2024.3350247","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Tang, Z., Xu, Y., Jiao, Z., Lu, J., Jin, L., Aibaidula, A., Wu, J., Wang, Q., Zhang, H., Shen, D.: Pre-operative overall survival time prediction for glioblastoma patients using deep learning on both imaging phenotype and genotype. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part I 22, pp. 415\u2013422. Springer (2019)","DOI":"10.1007\/978-3-030-32239-7_46"},{"issue":"6","key":"13_CR31","doi-asserted-by":"publisher","first-page":"2100","DOI":"10.1109\/TMI.2020.2964310","volume":"39","author":"Z Tang","year":"2020","unstructured":"Tang, Z., Xu, Y., Jin, L., Aibaidula, A., Lu, J., Jiao, Z., Wu, J., Zhang, H., Shen, D.: Deep learning of imaging phenotype and genotype for predicting overall survival time of glioblastoma patients. IEEE Trans. Med. Imaging 39(6), 2100\u20132109 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"13_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2022.110527","volume":"156","author":"H Wang","year":"2022","unstructured":"Wang, H., Liu, Y., Xu, N., Sun, Y., Fu, S., Wu, Y., Liu, C., Cui, L., Liu, Z., Chang, Z., et al.: Development and validation of a deep learning model for survival prognosis of transcatheter arterial chemoembolization in patients with intermediate-stage hepatocellular carcinoma. Eur. J. Radiol. 156, 110527 (2022)","journal-title":"Eur. J. Radiol."},{"key":"13_CR33","doi-asserted-by":"publisher","first-page":"1100087","DOI":"10.3389\/fonc.2023.1100087","volume":"13","author":"J Wang","year":"2023","unstructured":"Wang, J., Mao, Y., Gao, X., Zhang, Y.: Recurrence risk stratification for locally advanced cervical cancer using multi-modality transformer network. Front. Oncol. 13, 1100087 (2023)","journal-title":"Front. Oncol."},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Wang, W., Wang, F., Yang, Y., Li, Y., Liu, J., Han, X., Lin, L., Tong, R., Hu, H., Chen, Y.W.: Deep learning-based risk prediction model for recurrence-free survival in patients with hepatocellular carcinoma using multi-phase ct image. In: 2022 IEEE 11th Global Conference on Consumer Electronics (GCCE), pp. 926\u2013929. IEEE (2022)","DOI":"10.1109\/GCCE56475.2022.10014204"},{"key":"13_CR35","doi-asserted-by":"crossref","unstructured":"Wu, J.p., Ding, W.z., Wang, Y.l., Liu, S., Zhang, X.q., Yang, Q., Cai, W.j., Yu, X.l., Liu, F.y., Kong, D., et\u00a0al.: Radiomics analysis of ultrasound to predict recurrence of hepatocellular carcinoma after microwave ablation. Int. J. Hyperthermia 39(1), 595\u2013604 (2022)","DOI":"10.1080\/02656736.2022.2062463"},{"key":"13_CR36","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1007\/s00595-016-1320-x","volume":"46","author":"TH Wu","year":"2016","unstructured":"Wu, T.H., Hatano, E., Yamanaka, K., Seo, S., Taura, K., Yasuchika, K., Fujimoto, Y., Nitta, T., Mizumoto, M., Mori, A., et al.: A non-smooth tumor margin on preoperative imaging predicts microvascular invasion of hepatocellular carcinoma. Surg. Today 46, 1275\u20131281 (2016)","journal-title":"Surg. Today"},{"key":"13_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102150","volume":"73","author":"J Yao","year":"2021","unstructured":"Yao, J., et al.: Deepprognosis: preoperative prediction of pancreatic cancer survival and surgical margin via comprehensive understanding of dynamic contrast-enhanced ct imaging and tumor-vascular contact parsing. Med. Image Anal. 73, 102150 (2021)","journal-title":"Med. Image Anal."},{"issue":"3","key":"13_CR38","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1148\/radiol.2017170706","volume":"286","author":"K Yasaka","year":"2018","unstructured":"Yasaka, K., Akai, H., Abe, O., Kiryu, S.: Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced ct: a preliminary study. Radiology 286(3), 887\u2013896 (2018)","journal-title":"Radiology"},{"key":"13_CR39","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s11604-019-00817-3","volume":"37","author":"N Yoneda","year":"2019","unstructured":"Yoneda, N., Matsui, O., Kobayashi, S., Kitao, A., Kozaka, K., Inoue, D., Yoshida, K., Minami, T., Koda, W., Gabata, T.: Current status of imaging biomarkers predicting the biological nature of hepatocellular carcinoma. Jpn. J. Radiol. 37, 191\u2013208 (2019)","journal-title":"Jpn. J. Radiol."},{"key":"13_CR40","doi-asserted-by":"crossref","unstructured":"Zhan, G., Wang, F., Wang, W., Li, Y., Chen, Q., Hu, H., Chen, Y.W.: A transformer-based model for preoperative early recurrence prediction of hepatocellular carcinoma with muti-modality mri. In: Asian Conference on Computer Vision, pp. 185\u2013194. Springer (2022)","DOI":"10.1007\/978-3-031-27066-6_13"},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"Zhan, G., et\u00a0al.: A transformer-based model for preoperative early recurrence prediction of hepatocellular carcinoma with muti-modality mri. In: Computer Vision\u2013ACCV 2022 Workshops: 16th Asian Conference on Computer Vision, Macao, China, December 4\u20138, 2022, Revised Selected Papers, pp. 185\u2013194. Springer (2023)","DOI":"10.1007\/978-3-031-27066-6_13"},{"issue":"8","key":"13_CR42","doi-asserted-by":"publisher","first-page":"5181","DOI":"10.1007\/s00432-022-04467-3","volume":"149","author":"Y Zhao","year":"2023","unstructured":"Zhao, Y., Huang, F., Liu, S., Jian, L., Xia, X., Lin, H., Liu, J.: Prediction of therapeutic response of unresectable hepatocellular carcinoma to hepatic arterial infusion chemotherapy based on pretherapeutic mri radiomics and albumin-bilirubin score. J. Cancer Res. Clin. Oncol. 149(8), 5181\u20135192 (2023)","journal-title":"J. Cancer Res. Clin. Oncol."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8496-7_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T00:19:23Z","timestamp":1757117963000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8496-7_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"ISBN":["9789819784950","9789819784967"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8496-7_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,3]]},"assertion":[{"value":"3 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}