{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T18:05:57Z","timestamp":1758477957091,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031721199"},{"type":"electronic","value":"9783031721205"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72120-5_58","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:02:53Z","timestamp":1727870573000},"page":"623-633","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Semi-supervised Lymph Node Metastasis Classification with\u00a0Pathology-Guided Label Sharpening and\u00a0Two-Streamed Multi-scale Fusion"],"prefix":"10.1007","author":[{"given":"Haoshen","family":"Li","sequence":"first","affiliation":[]},{"given":"Yirui","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Dazhou","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Qinji","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Ke","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Le","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Xianghua","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qifeng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Dakai","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"58_CR1","doi-asserted-by":"crossref","unstructured":"Ajani, J.A., D\u2019Amico, T.A., Bentrem, D.J., Chao, J., Corvera, C., Das, P., Denlinger, C.S., Enzinger, P.C., Fanta, P., Farjah, F., et\u00a0al.: Esophageal and esophagogastric junction cancers, version 2.2019, nccn clinical practice guidelines in oncology. Journal of the National Comprehensive Cancer Network 17(7), 855\u2013883 (2019)","DOI":"10.6004\/jnccn.2019.0033"},{"key":"58_CR2","unstructured":"Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: Mixmatch: A holistic approach to semi-supervised learning. Advances in neural information processing systems 32 (2019)"},{"key":"58_CR3","doi-asserted-by":"crossref","unstructured":"Chao, C.H., Zhu, Z., Guo, D., Yan, K., Ho, T.Y., Cai, J., Harrison, A.P., Ye, X., Xiao, J., Yuille, A., et\u00a0al.: Lymph node gross tumor volume detection in oncology imaging via relationship learning using graph neural network. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 772\u2013782. Springer (2020)","DOI":"10.1007\/978-3-030-59728-3_75"},{"key":"58_CR4","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248\u2013255. Ieee (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"58_CR5","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"},{"key":"58_CR6","doi-asserted-by":"crossref","unstructured":"Holste, G., van\u00a0der Wal, D., Pinckaers, H., Yamashita, R., Mitani, A., Esteva, A.: Improved multimodal fusion for small datasets with auxiliary supervision. In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). pp.\u00a01\u20135. IEEE (2023)","DOI":"10.1109\/ISBI53787.2023.10230356"},{"key":"58_CR7","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et\u00a0al.: Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"issue":"4","key":"58_CR8","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.jncc.2022.09.003","volume":"2","author":"D Jin","year":"2022","unstructured":"Jin, D., Guo, D., Ge, J., Ye, X., Lu, L.: Towards automated organs at risk and target volumes contouring: Defining precision radiation therapy in the modern era. Journal of the National Cancer Center 2(4), 306\u2013313 (2022)","journal-title":"Journal of the National Cancer Center"},{"key":"58_CR9","doi-asserted-by":"crossref","unstructured":"Jin, D., Guo, D., Ho, T.Y., Harrison, A.P., Xiao, J., Tseng, C.k., Lu, L.: Deep esophageal clinical target volume delineation using encoded 3d spatial context of tumors, lymph nodes, and organs at risk. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part VI 22. pp. 603\u2013612. Springer (2019)","DOI":"10.1007\/978-3-030-32226-7_67"},{"key":"58_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101909","volume":"68","author":"D Jin","year":"2021","unstructured":"Jin, D., Guo, D., Ho, T.Y., Harrison, A.P., Xiao, J., Tseng, C.K., Lu, L.: Deeptarget: Gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy. Medical Image Analysis 68, 101909 (2021)","journal-title":"Medical Image Analysis"},{"issue":"1","key":"58_CR11","doi-asserted-by":"publisher","first-page":"14036","DOI":"10.1038\/s41598-018-32441-y","volume":"8","author":"BH Kann","year":"2018","unstructured":"Kann, B.H., Aneja, S., Loganadane, G.V., Kelly, J.R., Smith, S.M., Decker, R.H., Yu, J.B., Park, H.S., Yarbrough, W.G., Malhotra, A., et\u00a0al.: Pretreatment identification of head and neck cancer nodal metastasis and extranodal extension using deep learning neural networks. Scientific reports 8(1), 14036 (2018)","journal-title":"Scientific reports"},{"issue":"12","key":"58_CR12","doi-asserted-by":"publisher","first-page":"1304","DOI":"10.1200\/JCO.19.02031","volume":"38","author":"BH Kann","year":"2020","unstructured":"Kann, B.H., Hicks, D.F., Payabvash, S., Mahajan, A., Du, J., Gupta, V., Park, H.S., Yu, J.B., Yarbrough, W.G., Burtness, B.A., et\u00a0al.: Multi-institutional validation of deep learning for pretreatment identification of extranodal extension in head and neck squamous cell carcinoma. Journal of Clinical Oncology 38(12), 1304\u20131311 (2020)","journal-title":"Journal of Clinical Oncology"},{"issue":"2","key":"58_CR13","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1109\/JBHI.2018.2824327","volume":"23","author":"J Kawahara","year":"2018","unstructured":"Kawahara, J., Daneshvar, S., Argenziano, G., Hamarneh, G.: Seven-point checklist and skin lesion classification using multitask multimodal neural nets. IEEE journal of biomedical and health informatics 23(2), 538\u2013546 (2018)","journal-title":"IEEE journal of biomedical and health informatics"},{"key":"58_CR14","unstructured":"Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242 (2016)"},{"key":"58_CR15","doi-asserted-by":"publisher","first-page":"5452","DOI":"10.1007\/s00330-019-06098-8","volume":"29","author":"JH Lee","year":"2019","unstructured":"Lee, J.H., Ha, E.J., Kim, J.H.: Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with ct. European radiology 29, 5452\u20135457 (2019)","journal-title":"European radiology"},{"key":"58_CR16","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"issue":"2","key":"58_CR17","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1148\/radiology.182.2.1732943","volume":"182","author":"T McLoud","year":"1992","unstructured":"McLoud, T., Bourgouin, P., Greenberg, R., Kosiuk, J., Templeton, P., Shepard, J.A., Moore, E., Wain, J., Mathisen, D., Grillo, H.: Bronchogenic carcinoma: analysis of staging in the mediastinum with ct by correlative lymph node mapping and sampling. Radiology 182(2), 319\u2013323 (1992)","journal-title":"Radiology"},{"key":"58_CR18","unstructured":"Mehta, S., Rastegari, M.: Separable self-attention for mobile vision transformers. arXiv preprint arXiv:2206.02680 (2022)"},{"issue":"5","key":"58_CR19","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1109\/TMI.2015.2482920","volume":"35","author":"HR Roth","year":"2015","unstructured":"Roth, H.R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., Summers, R.M.: Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE transactions on medical imaging 35(5), 1170\u20131181 (2015)","journal-title":"IEEE transactions on medical imaging"},{"key":"58_CR20","doi-asserted-by":"crossref","unstructured":"Roth, H.R., Lu, L., Seff, A., Cherry, K.M., Hoffman, J., Wang, S., Liu, J., Turkbey, E., Summers, R.M.: A new 2.5 d representation for lymph node detection using random sets of deep convolutional neural network observations. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part I 17. pp. 520\u2013527. Springer (2014)","DOI":"10.1007\/978-3-319-10404-1_65"},{"key":"58_CR21","doi-asserted-by":"crossref","unstructured":"Schwartz, L., Bogaerts, J., Ford, R., Shankar, L., Therasse, P., Gwyther, S., Eisenhauer, E.: Evaluation of lymph nodes with recist 1.1. European journal of cancer 45(2), 261\u2013267 (2009)","DOI":"10.1016\/j.ejca.2008.10.028"},{"key":"58_CR22","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Annual review of biomedical engineering 19, 221\u2013248 (2017)","journal-title":"Annual review of biomedical engineering"},{"key":"58_CR23","doi-asserted-by":"crossref","unstructured":"Siegel, R.L., Miller, K.D., Fuchs, H.E., Jemal, A.: Cancer statistics, 2022. CA: a cancer journal for clinicians 72(1), 7\u201333 (2022)","DOI":"10.3322\/caac.21708"},{"key":"58_CR24","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems 30 (2017)"},{"key":"58_CR25","unstructured":"Yan, K., Cai, J., Zheng, Y., Harrison, A.P., Jin, D., Tang, Y.B., Tang, Y.X., Huang, L., Xiao, J., Lu, L.: Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT. IEEE Trans. Med. Imaging 2020, \u00a01 (sep 2020)"},{"key":"58_CR26","doi-asserted-by":"crossref","unstructured":"Zheng, M., You, S., Huang, L., Wang, F., Qian, C., Xu, C.: Simmatch: Semi-supervised learning with similarity matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 14471\u201314481 (2022)","DOI":"10.1109\/CVPR52688.2022.01407"},{"issue":"5","key":"58_CR27","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1109\/JPROC.2021.3054390","volume":"109","author":"SK Zhou","year":"2021","unstructured":"Zhou, S.K., Greenspan, H., Davatzikos, C., Duncan, J.S., Van\u00a0Ginneken, B., Madabhushi, A., Prince, J.L., Rueckert, D., Summers, R.M.: A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. Proceedings of the IEEE 109(5), 820\u2013838 (2021)","journal-title":"Proceedings of the IEEE"},{"key":"58_CR28","unstructured":"Zhu, Z., Yan, K., Jin, D., Cai, J., Ho, T.Y., Harrison, A.P., Guo, D., Chao, C.H., Ye, X., Xiao, J., et\u00a0al.: Detecting scatteredly-distributed, small, andcritically important objects in 3d oncologyimaging via decision stratification. arXiv preprint arXiv:2005.13705 (2020)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72120-5_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:29:02Z","timestamp":1727872142000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72120-5_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721199","9783031721205"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72120-5_58","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"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":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 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":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}