{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T01:48:41Z","timestamp":1771033721021,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164361","type":"print"},{"value":"9783031164378","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16437-8_51","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T18:13:04Z","timestamp":1663265584000},"page":"534-543","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Spatiotemporal Attention for\u00a0Early Prediction of\u00a0Hepatocellular Carcinoma Based on\u00a0Longitudinal Ultrasound Images"],"prefix":"10.1007","author":[{"given":"Yiwen","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengguang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liming","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangda","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiarun","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanping","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"issue":"4","key":"51_CR1","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"},{"key":"51_CR2","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"51_CR3","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9268\u20139277 (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"51_CR4","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":"8","key":"51_CR5","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"51_CR6","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"9","key":"51_CR7","doi-asserted-by":"publisher","first-page":"e2015626","DOI":"10.1001\/jamanetworkopen.2020.15626","volume":"3","author":"GN Ioannou","year":"2020","unstructured":"Ioannou, G.N., et al.: Assessment of a deep learning model to predict hepatocellular carcinoma in patients with hepatitis c cirrhosis. JAMA Netw. Open 3(9), e2015626\u2013e2015626 (2020)","journal-title":"JAMA Netw. Open"},{"issue":"6","key":"51_CR8","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.3390\/ijms20061358","volume":"20","author":"T Kanda","year":"2019","unstructured":"Kanda, T., Goto, T., Hirotsu, Y., Moriyama, M., Omata, M.: Molecular mechanisms driving progression of liver cirrhosis towards hepatocellular carcinoma in chronic hepatitis b and c infections: a review. Int. J. Mol. Sci. 20(6), 1358 (2019)","journal-title":"Int. J. Mol. Sci."},{"key":"51_CR9","doi-asserted-by":"crossref","unstructured":"Li, X., Zhong, Z., Wu, J., Yang, Y., Lin, Z., Liu, H.: Expectation-maximization attention networks for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9167\u20139176 (2019)","DOI":"10.1109\/ICCV.2019.00926"},{"issue":"2","key":"51_CR10","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1002\/hep.29913","volume":"68","author":"JA Marrero","year":"2018","unstructured":"Marrero, J.A., et al.: Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by the American association for the study of liver diseases. Hepatology 68(2), 723\u2013750 (2018)","journal-title":"Hepatology"},{"key":"51_CR11","doi-asserted-by":"crossref","unstructured":"Obi, S., et al.: Combination therapy of intraarterial 5-fluorouracil and systemic interferon-alpha for advanced hepatocellular carcinoma with portal venous invasion. Cancer Interdisc. Int. J. Am. Cancer Soc. 106(9), 1990\u20131997 (2006)","DOI":"10.1002\/cncr.21832"},{"issue":"4","key":"51_CR12","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1016\/j.jhep.2015.11.035","volume":"64","author":"G Papatheodoridis","year":"2016","unstructured":"Papatheodoridis, G., et al.: PAGE-B predicts the risk of developing hepatocellular carcinoma in Caucasians with chronic hepatitis B on 5-year antiviral therapy. J. Hepatol. 64(4), 800\u2013806 (2016)","journal-title":"J. Hepatol."},{"key":"51_CR13","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.jbi.2017.04.001","volume":"69","author":"T Pham","year":"2017","unstructured":"Pham, T., Tran, T., Phung, D., Venkatesh, S.: Predicting healthcare trajectories from medical records: a deep learning approach. J. Biomed. Inform. 69, 218\u2013229 (2017)","journal-title":"J. Biomed. Inform."},{"issue":"5","key":"51_CR14","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1136\/gutjnl-2015-310818","volume":"65","author":"Z Poh","year":"2016","unstructured":"Poh, Z., et al.: Real-world risk score for hepatocellular carcinoma (RWS-HCC): a clinically practical risk predictor for HCC in chronic hepatitis B. Gut 65(5), 887\u2013888 (2016)","journal-title":"Gut"},{"issue":"11","key":"51_CR15","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673\u20132681 (1997)","journal-title":"IEEE Trans. Signal Process."},{"key":"51_CR16","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"issue":"21","key":"51_CR17","doi-asserted-by":"publisher","first-page":"3707","DOI":"10.1093\/bioinformatics\/btab482","volume":"37","author":"D Sharma","year":"2021","unstructured":"Sharma, D., Xu, W.: phyLoSTM: a novel deep learning model on disease prediction from longitudinal microbiome data. Bioinformatics 37(21), 3707\u20133714 (2021)","journal-title":"Bioinformatics"},{"key":"51_CR18","unstructured":"Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"51_CR19","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"issue":"04","key":"51_CR20","doi-asserted-by":"publisher","first-page":"1950022","DOI":"10.1142\/S0219720019500227","volume":"17","author":"H Wang","year":"2019","unstructured":"Wang, H., Li, C., Zhang, J., Wang, J., Ma, Y., Lian, Y.: A new LSTM-based gene expression prediction model: L-GEPM. J. Bioinform. Comput. Biol. 17(04), 1950022 (2019)","journal-title":"J. Bioinform. Comput. Biol."},{"key":"51_CR21","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"issue":"2","key":"51_CR22","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.jhep.2013.09.029","volume":"60","author":"GLH Wong","year":"2014","unstructured":"Wong, G.L.H., et al.: Liver stiffness-based optimization of hepatocellular carcinoma risk score in patients with chronic hepatitis B. J. Hepatol. 60(2), 339\u2013345 (2014)","journal-title":"J. Hepatol."},{"issue":"12","key":"51_CR23","doi-asserted-by":"publisher","first-page":"2499","DOI":"10.1016\/j.cgh.2021.02.040","volume":"19","author":"S Wu","year":"2021","unstructured":"Wu, S., et al.: Hepatocellular carcinoma prediction models in chronic hepatitis B: a systematic review of 14 models and external validation. Clin. Gastroenterol. Hepatol. 19(12), 2499\u20132513 (2021)","journal-title":"Clin. Gastroenterol. Hepatol."},{"issue":"10","key":"51_CR24","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1038\/s41575-019-0186-y","volume":"16","author":"JD Yang","year":"2019","unstructured":"Yang, J.D., Hainaut, P., Gores, G.J., Amadou, A., Plymoth, A., Roberts, L.R.: A global view of hepatocellular carcinoma: trends, risk, prevention and management. Nat. Rev. Gastroenterol. Hepatol. 16(10), 589\u2013604 (2019)","journal-title":"Nat. Rev. Gastroenterol. Hepatol."},{"issue":"1","key":"51_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12885-018-5003-4","volume":"18","author":"Z Yao","year":"2018","unstructured":"Yao, Z., et al.: Preoperative diagnosis and prediction of hepatocellular carcinoma: radiomics analysis based on multi-modal ultrasound images. BMC Cancer 18(1), 1\u201311 (2018)","journal-title":"BMC Cancer"},{"key":"51_CR26","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16437-8_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T14:09:39Z","timestamp":1710252579000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16437-8_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164361","9783031164378"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16437-8_51","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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":"5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}