{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T05:48:43Z","timestamp":1781848123582,"version":"3.54.5"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030872397","type":"print"},{"value":"9783030872403","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87240-3_63","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T07:44:03Z","timestamp":1632383043000},"page":"657-666","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Co-graph Attention Reasoning Based Imaging and Clinical Features Integration for Lymph Node Metastasis Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8224-4698","authenticated-orcid":false,"given":"Hui","family":"Cui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5328-691X","authenticated-orcid":false,"given":"Ping","family":"Xuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1781-1067","authenticated-orcid":false,"given":"Qiangguo","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingjun","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Butuo","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiyue","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingjie","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanlong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinming","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linlin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4808-6109","authenticated-orcid":false,"given":"Been-Lirn","family":"Duh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"63_CR1","doi-asserted-by":"publisher","first-page":"20190827","DOI":"10.1259\/bjr.20190827","volume":"93","author":"HN Lee","year":"2020","unstructured":"Lee, H.N., Kim, J.I., Shin, S.Y., Kim, D.H., Kim, C., Hong, I.K.: Combined CT texture analysis and nodal axial ratio for detection of nodal metastasis in esophageal cancer. Br. J. Radiol. 93, 20190827 (2020)","journal-title":"Br. J. Radiol."},{"key":"63_CR2","unstructured":"Cancer Stat Facts: Esophageal Cancer. National Cancer Institute. https:\/\/seer.cancer.gov\/statfacts\/html\/esoph.html"},{"key":"63_CR3","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1186\/s40644-019-0225-5","volume":"19","author":"JY Lee","year":"2019","unstructured":"Lee, J.Y., et al.: Improved detection of metastatic lymph nodes in oesophageal squamous cell carcinoma by combined interpretation of fluorine-18-fluorodeoxyglucose positron-emission tomography\/computed tomography. Cancer Imaging 19, 40 (2019)","journal-title":"Cancer Imaging"},{"issue":"1","key":"63_CR4","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1007\/s00330-017-4935-4","volume":"28","author":"J Liu","year":"2017","unstructured":"Liu, J., Wang, Z., Shao, H., Qu, D., Liu, J., Yao, L.: Improving CT detection sensitivity for nodal metastases in oesophageal cancer with combination of smaller size and lymph node axial ratio. Eur. Radiol. 28(1), 188\u2013195 (2017). https:\/\/doi.org\/10.1007\/s00330-017-4935-4","journal-title":"Eur. Radiol."},{"key":"63_CR5","first-page":"693","volume":"72","author":"K Foley","year":"2017","unstructured":"Foley, K., Christian, A., Fielding, P., Lewis, W., Roberts, S.: Accuracy of contemporary oesophageal cancer lymph node staging with radiological-pathological correlation. J Clin. Radiol. 72, 693-e691 (2017)","journal-title":"J Clin. Radiol."},{"key":"63_CR6","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.3389\/fonc.2019.01548","volume":"9","author":"L Wu","year":"2019","unstructured":"Wu, L., et al.: Multiple level CT radiomics features preoperatively predict lymph node metastasis in esophageal cancer: a multicentre retrospective study. Front Oncol 9, 1548 (2019)","journal-title":"Front Oncol"},{"key":"63_CR7","doi-asserted-by":"publisher","first-page":"5471","DOI":"10.1088\/0031-9155\/60\/14\/5471","volume":"60","author":"M Vallieres","year":"2015","unstructured":"Vallieres, M., Freeman, C.R., Skamene, S.R., El Naqa, I.: A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60, 5471\u20135496 (2015)","journal-title":"Phys Med Biol"},{"key":"63_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/978-3-030-59713-9_51","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"G Chauhan","year":"2020","unstructured":"Chauhan, G., et al.: Joint modeling of chest radiographs and radiology reports for pulmonary edema assessment. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 529\u2013539. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_51"},{"key":"63_CR9","doi-asserted-by":"publisher","first-page":"11399","DOI":"10.1038\/s41598-019-47765-6","volume":"9","author":"A Sharma","year":"2019","unstructured":"Sharma, A., Vans, E., Shigemizu, D., Boroevich, K.A., Tsunoda, T.: DeepInsight: a methodology to transform a non-image data to an image for convolution neural network architecture. Sci. Rep. 9, 11399 (2019)","journal-title":"Sci. Rep."},{"key":"63_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zhu, Y., Zhang, W., Zhu, Y.: Cross-modal image sentiment analysis via deep correlation of textual semantic. Knowl.-Based Syst. 216 (2021)","DOI":"10.1016\/j.knosys.2021.106803"},{"key":"63_CR11","doi-asserted-by":"publisher","first-page":"3224","DOI":"10.1109\/TMM.2020.2971171","volume":"22","author":"L Ye","year":"2020","unstructured":"Ye, L., Liu, Z., Wang, Y.: Dual convolutional LSTM network for referring image segmentation. IEEE Trans. Multimedia 22, 3224\u20133235 (2020)","journal-title":"IEEE Trans. Multimedia"},{"key":"63_CR12","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32, 4\u201324 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"63_CR13","doi-asserted-by":"crossref","unstructured":"Xuan, P., Zhang, Y., Cui, H., Zhang, T., Guo, M., Nakaguchi, T.: Integrating multi-scale neighbouring topologies and cross-modal similarities for drug\u2013protein interaction prediction. Briefings in Bioinf. 119 (2021)","DOI":"10.1093\/bib\/bbab119"},{"key":"63_CR14","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. Int. Conf. Learn. Rep. (2018)"},{"key":"63_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1007\/978-3-030-59719-1_42","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"S Mo","year":"2020","unstructured":"Mo, S., et al.: Multimodal priors guided segmentation of liver lesions in MRI using mutual information based graph co-attention networks. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 429\u2013438. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_42"},{"key":"63_CR16","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"63_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fbioe.2020.00001","volume":"8","author":"Q Jin","year":"2020","unstructured":"Jin, Q., Meng, Z., Sun, C., Cui, H., Su, R.: RA-UNet: a hybrid deep attention-aware network to extract liver and tumor in CT scans. Front. Bioeng. Biotechnol. 8, 1\u201315 (2020)","journal-title":"Front. Bioeng. Biotechnol."},{"key":"63_CR18","doi-asserted-by":"crossref","unstructured":"Sheng, N., Cui, H., Zhang, T., Xuan, P.: Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA-disease association prediction. Briefings Bioinf. 22 (2020)","DOI":"10.1093\/bib\/bbaa067"},{"key":"63_CR19","doi-asserted-by":"crossref","unstructured":"Wang, T., Wang, G., Tan, K.E., Tan, D.: Spectral Pyramid Graph Attention Network for Hyperspectral Image Classification. arXiv preprint arXiv:2001.07108 (2020)","DOI":"10.1007\/978-3-030-64556-4_55"},{"key":"63_CR20","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper, J., et al.: Attention gated networks: learning to leverage salient regions in medical images. Med Image Anal 53, 197\u2013207 (2019)","journal-title":"Med Image Anal"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87240-3_63","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,4]],"date-time":"2021-12-04T23:08:13Z","timestamp":1638659293000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87240-3_63"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872397","9783030872403"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87240-3_63","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/en\/","order":11,"name":"conference_url","label":"Conference URL","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 CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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":"4","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)"}},{"value":"The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}