{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T09:49:14Z","timestamp":1758707354459,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031439032"},{"type":"electronic","value":"9783031439049"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43904-9_35","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"358-367","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["CARL: Cross-Aligned Representation Learning for Multi-view Lung Cancer Histology Classification"],"prefix":"10.1007","author":[{"given":"Yin","family":"Luo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qilong","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuhong","family":"Min","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minghui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Aerts, H.J.W.L. et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative Radiomics approach. Nat. Commun. 5(1), 4006 (2014). https:\/\/doi.org\/10.1038\/ncomms5006","key":"35_CR1","DOI":"10.1038\/ncomms5006"},{"doi-asserted-by":"publisher","unstructured":"Bakr, S. et al.: A radiogenomic dataset of non-small cell lung cancer. Sci. Data 5(1), 180202 (2018). https:\/\/doi.org\/10.1038\/sdata.2018.202","key":"35_CR2","DOI":"10.1038\/sdata.2018.202"},{"issue":"1","key":"35_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"doi-asserted-by":"publisher","unstructured":"Chaunzwa, T.L. et al.: Deep learning classification of lung cancer histology using CT images. Sci. Rep. 11(1), 5471 (2021). https:\/\/doi.org\/10.1038\/s41598-021-84630-x","key":"35_CR4","DOI":"10.1038\/s41598-021-84630-x"},{"issue":"6","key":"35_CR5","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark, K., et al.: The cancer imaging archive (TCIA): Maintaining and operating a public information repository. J. Digit Imaging. 26(6), 1045\u20131057 (2013). https:\/\/doi.org\/10.1007\/s10278-013-9622-7","journal-title":"J. Digit Imaging."},{"issue":"3","key":"35_CR6","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995). https:\/\/doi.org\/10.1007\/BF00994018","journal-title":"Mach. Learn."},{"issue":"7","key":"35_CR7","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.1109\/TBME.2016.2613502","volume":"64","author":"Q Dou","year":"2017","unstructured":"Dou, Q., et al.: Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans. Biomed. Eng. 64(7), 1558\u20131567 (2017). https:\/\/doi.org\/10.1109\/TBME.2016.2613502","journal-title":"IEEE Trans. Biomed. Eng."},{"doi-asserted-by":"publisher","unstructured":"Feng, Y., et al.: GVCNN: Group-view convolutional neural networks for 3D shape recognition. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 264\u2013272 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00035","key":"35_CR8","DOI":"10.1109\/CVPR.2018.00035"},{"issue":"9","key":"35_CR9","doi-asserted-by":"publisher","first-page":"e258","DOI":"10.1016\/j.acra.2020.06.010","volume":"28","author":"Y Guo","year":"2021","unstructured":"Guo, Y., et al.: Histological subtypes classification of lung cancers on CT images using 3D deep learning and radiomics. Acad. Radiol. 28(9), e258\u2013e266 (2021). https:\/\/doi.org\/10.1016\/j.acra.2020.06.010","journal-title":"Acad. Radiol."},{"doi-asserted-by":"publisher","unstructured":"He, K., et al.: Deep Residual Learning for Image Recognition. http:\/\/arxiv.org\/abs\/1512.03385 (2015). https:\/\/doi.org\/10.48550\/arXiv.1512.03385","key":"35_CR10","DOI":"10.48550\/arXiv.1512.03385"},{"issue":"3","key":"35_CR11","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1109\/TCBB.2020.2970713","volume":"18","author":"C Li","year":"2021","unstructured":"Li, C., et al.: Multi-view mammographic density classification by dilated and attention-guided residual learning. IEEE\/ACM Trans. Comput. Biol. Bioinf. 18(3), 1003\u20131013 (2021). https:\/\/doi.org\/10.1109\/TCBB.2020.2970713","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"8","key":"35_CR12","doi-asserted-by":"publisher","first-page":"4123","DOI":"10.1109\/JBHI.2022.3161466","volume":"26","author":"S Li","year":"2022","unstructured":"Li, S., et al.: Adaptive multimodal fusion with attention guided deep supervision net for grading hepatocellular carcinoma. IEEE J. Biomed. Health Inform. 26(8), 4123\u20134131 (2022). https:\/\/doi.org\/10.1109\/JBHI.2022.3161466","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"35_CR13","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s11517-020-02302-w","volume":"59","author":"P Marentakis","year":"2021","unstructured":"Marentakis, P., et al.: Lung cancer histology classification from CT images based on radiomics and deep learning models. Med. Biol. Eng. Comput. 59(1), 215\u2013226 (2021). https:\/\/doi.org\/10.1007\/s11517-020-02302-w","journal-title":"Med. Biol. Eng. Comput."},{"doi-asserted-by":"publisher","unstructured":"Meng, Z., et al.: MSMFN: an ultrasound based multi-step modality fusion network for identifying the histologic subtypes of metastatic cervical lymphadenopathy. In: IEEE Transactions on Medical Imaging, pp. 1\u20131 (2022). https:\/\/doi.org\/10.1109\/TMI.2022.3222541","key":"35_CR14","DOI":"10.1109\/TMI.2022.3222541"},{"doi-asserted-by":"publisher","unstructured":"Pereira, T. et al.: Comprehensive perspective for lung cancer characterisation based on AI solutions using CT images. J. Clin. Med. 10(1), 118 (2021). https:\/\/doi.org\/10.3390\/jcm10010118","key":"35_CR15","DOI":"10.3390\/jcm10010118"},{"issue":"3","key":"35_CR16","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1109\/JBHI.2018.2879834","volume":"23","author":"P Sahu","year":"2019","unstructured":"Sahu, P., et al.: A lightweight multi-section CNN for lung nodule classification and malignancy estimation. IEEE J. Biomed. Health Inform. 23(3), 960\u2013968 (2019). https:\/\/doi.org\/10.1109\/JBHI.2018.2879834","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"2","key":"35_CR17","doi-asserted-by":"publisher","first-page":"140","DOI":"10.6063\/motricidade.6470","volume":"12","author":"JA Sedrez","year":"2016","unstructured":"Sedrez, J.A., et al.: Non-invasive postural assessment of the spine in the sagittal plane: a systematic review. Motricidade 12(2), 140\u2013154 (2016). https:\/\/doi.org\/10.6063\/motricidade.6470","journal-title":"Motricidade"},{"doi-asserted-by":"publisher","unstructured":"Su, H., et al.: Multi-view convolutional neural networks for 3D shape recognition. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 945\u2013953 (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.114","key":"35_CR18","DOI":"10.1109\/ICCV.2015.114"},{"doi-asserted-by":"publisher","unstructured":"Su, R., et al.: Identification of expression signatures for non-small-cell lung carcinoma subtype classification. Bioinformatics 36(2), 339\u2013346 (2019). https:\/\/doi.org\/10.1093\/bioinformatics\/btz557","key":"35_CR19","DOI":"10.1093\/bioinformatics\/btz557"},{"key":"35_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105691","volume":"146","author":"S Tomassini","year":"2022","unstructured":"Tomassini, S., et al.: Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: a survey. Comput. Biol. Med. 146, 105691 (2022). https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105691","journal-title":"Comput. Biol. Med."},{"doi-asserted-by":"publisher","unstructured":"Wang, J., et al.: UASSR: unsupervised arbitrary scale super-resolution reconstruction of\u00a0single anisotropic 3D images via\u00a0disentangled representation learning. In: Wang, L. et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022, pp. 453\u2013462 Springer Nature Switzerland, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16446-0_43","key":"35_CR21","DOI":"10.1007\/978-3-031-16446-0_43"},{"key":"35_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.109041","volume":"128","author":"X Wu","year":"2020","unstructured":"Wu, X., et al.: Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study. Eur. J. Radiol. 128, 109041 (2020). https:\/\/doi.org\/10.1016\/j.ejrad.2020.109041","journal-title":"Eur. J. Radiol."},{"issue":"4","key":"35_CR23","doi-asserted-by":"publisher","first-page":"1978","DOI":"10.1007\/s00330-020-07339-x","volume":"31","author":"M Yanagawa","year":"2021","unstructured":"Yanagawa, M., et al.: Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network. Eur. Radiol. 31(4), 1978\u20131986 (2021). https:\/\/doi.org\/10.1007\/s00330-020-07339-x","journal-title":"Eur. Radiol."},{"unstructured":"Zellinger, W., et al.: Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning. http:\/\/arxiv.org\/abs\/1702.08811 (2019)","key":"35_CR24"},{"doi-asserted-by":"publisher","unstructured":"Zhang, N., et al.: Circular RNA circSATB2 promotes progression of non-small cell lung cancer cells. Mol. Cancer. 19(1), 101 (2020). https:\/\/doi.org\/10.1186\/s12943-020-01221-6","key":"35_CR25","DOI":"10.1186\/s12943-020-01221-6"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43904-9_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T14:39:24Z","timestamp":1710167964000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43904-9_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439032","9783031439049"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43904-9_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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)"}}]}}