{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T15:38:30Z","timestamp":1772206710791,"version":"3.50.1"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030597153","type":"print"},{"value":"9783030597160","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-59716-0_66","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T20:03:41Z","timestamp":1601669021000},"page":"690-699","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Spectral-spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification"],"prefix":"10.1007","author":[{"given":"Marcel","family":"Bengs","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nils","family":"Gessert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wiebke","family":"Laffers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dennis","family":"Eggert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephan","family":"Westermann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nina A.","family":"Mueller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas O. H.","family":"Gerstner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Betz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Schlaefer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"issue":"4","key":"66_CR1","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/s10585-018-9896-8","volume":"35","author":"M Alieva","year":"2018","unstructured":"Alieva, M., van Rheenen, J., Broekman, M.L.D.: Potential impact of invasive surgical procedures on primary tumor growth and metastasis. Clin. Exp. Metastasis 35(4), 319\u2013331 (2018). https:\/\/doi.org\/10.1007\/s10585-018-9896-8","journal-title":"Clin. Exp. Metastasis"},{"issue":"6","key":"66_CR2","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1007\/s00405-007-0251-y","volume":"264","author":"C Arens","year":"2007","unstructured":"Arens, C., Reussner, D., Woenkhaus, J., Leunig, A., Betz, C., Glanz, H.: Indirect fluorescence laryngoscopy in the diagnosis of precancerous and cancerous laryngeal lesions. Eur. Arch. Oto-rhino-laryngology. 264(6), 621\u2013626 (2007)","journal-title":"Eur. Arch. Oto-rhino-laryngology."},{"key":"66_CR3","doi-asserted-by":"crossref","unstructured":"Bengs, M., et al.: Spatio-spectral deep learning methods forin-vivohyperspectral laryngeal cancer detection. In: SPIE Medical Imaging 2020: Computer-Aided Diagnosis. p. in print (2020)","DOI":"10.1117\/12.2549251"},{"key":"66_CR4","unstructured":"Chen, Q., Ling, Z.H., Zhu, X.: Enhancing sentence embedding with generalized pooling. In: COLING (2018)"},{"key":"66_CR5","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"66_CR6","doi-asserted-by":"publisher","DOI":"10.1201\/9780429246593","volume-title":"An Introduction to the Bootstrap","author":"B Efron","year":"1994","unstructured":"Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. CRC Press, Boca Raton (1994)"},{"key":"66_CR7","doi-asserted-by":"crossref","unstructured":"Eggert, D., et al.: In vivo detection of laryngeal cancer by hyperspectral imaging combined with deep learning methods (conference presentation). In: Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2020, vol. 11213, p. 112130L. International Society for Optics and Photonics (2020)","DOI":"10.1117\/12.2557496"},{"issue":"3","key":"66_CR8","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1002\/jbio.201100081","volume":"5","author":"AO Gerstner","year":"2012","unstructured":"Gerstner, A.O., et al.: Hyperspectral imaging of mucosal surfaces in patients. J. Biophotonics 5(3), 255\u2013262 (2012)","journal-title":"J. Biophotonics"},{"issue":"9","key":"66_CR9","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1007\/s11548-019-02006-z","volume":"14","author":"N Gessert","year":"2019","unstructured":"Gessert, N., et al.: Spatio-temporal deep learning models for tip force estimation during needle insertion. Int. J. Comput. Assist. Radiol. Surg. 14(9), 1485\u20131493 (2019). https:\/\/doi.org\/10.1007\/s11548-019-02006-z","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"1","key":"66_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-60574-6","volume":"10","author":"A Grigoroiu","year":"2020","unstructured":"Grigoroiu, A., Yoon, J., Bohndiek, S.E.: Deep learning applied to hyperspectral endoscopy for online spectral classification. Sci. Rep. 10(1), 1\u201310 (2020)","journal-title":"Sci. Rep."},{"issue":"4","key":"66_CR11","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1097\/00005537-200104000-00018","volume":"111","author":"W Habermann","year":"2001","unstructured":"Habermann, W., Berghold, A., J Devaney, T.T., Friedrich, G.: Carcinoma of the larynx: predictors of diagnostic delay. Laryngoscope 111(4), 653\u2013656 (2001)","journal-title":"Laryngoscope"},{"key":"66_CR12","doi-asserted-by":"crossref","unstructured":"Halicek, M., et al.: Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks. In: Optical Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2018, vol. 10469, p. 104690X. International Society for Optics and Photonics (2018)","DOI":"10.1117\/12.2289023"},{"issue":"6","key":"66_CR13","doi-asserted-by":"publisher","first-page":"060503","DOI":"10.1117\/1.JBO.22.6.060503","volume":"22","author":"M Halicek","year":"2017","unstructured":"Halicek, M., et al.: Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging. J. Biomed. Opt. 22(6), 060503 (2017)","journal-title":"J. Biomed. Opt."},{"key":"66_CR14","doi-asserted-by":"publisher","first-page":"36S","DOI":"10.14219\/jada.archive.2001.0387","volume":"132","author":"AM Horowitz","year":"2001","unstructured":"Horowitz, A.M.: Perform a death-defying act: the 90-second oral cancer examination. J. Am. Den. Assoc. 132, 36S\u201340S (2001)","journal-title":"J. Am. Den. Assoc."},{"key":"66_CR15","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, pp. 2261\u20132269 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"issue":"1","key":"66_CR16","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s00106-015-0109-3","volume":"64","author":"W Laffers","year":"2016","unstructured":"Laffers, W., et al.: Early recognition of cancerous lesions in the mouth and oropharynx: automated evaluation of hyperspectral image stacks. HNO 64(1), 27\u201333 (2016)","journal-title":"HNO"},{"issue":"12","key":"66_CR17","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.3390\/rs9121330","volume":"9","author":"Q Liu","year":"2017","unstructured":"Liu, Q., Zhou, F., Hang, R., Yuan, X.: Bidirectional-convolutional LSTM based spectral-spatial feature learning for hyperspectral image classification. Remote Sens. 9(12), 1330 (2017)","journal-title":"Remote Sens."},{"issue":"5","key":"66_CR18","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1007\/s00405-013-2738-z","volume":"271","author":"J L\u00f6hler","year":"2014","unstructured":"L\u00f6hler, J., Gerstner, A., Bootz, F., Walther, L.: Incidence and localization of abnormal mucosa findings in patients consulting ent outpatient clinics and data analysis of a cancer registry. Eur. Arch. Oto-Rhino-Laryngology. 271(5), 1289\u20131297 (2014)","journal-title":"Eur. Arch. Oto-Rhino-Laryngology."},{"issue":"2","key":"66_CR19","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vis."},{"issue":"3","key":"66_CR20","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1002\/jbio.201500151","volume":"9","author":"B Regeling","year":"2016","unstructured":"Regeling, B., et al.: Development of an image pre-processor for operational hyperspectral laryngeal cancer detection. J. Biophotonics 9(3), 235\u2013245 (2016)","journal-title":"J. Biophotonics"},{"issue":"8","key":"66_CR21","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.3390\/s16081288","volume":"16","author":"B Regeling","year":"2016","unstructured":"Regeling, B., et al.: Hyperspectral imaging using flexible endoscopy for laryngeal cancer detection. Sensors 16(8), 1288 (2016)","journal-title":"Sensors"},{"issue":"1","key":"66_CR22","doi-asserted-by":"publisher","first-page":"51","DOI":"10.3322\/caac.21384","volume":"67","author":"KD Shield","year":"2017","unstructured":"Shield, K.D., et al.: The global incidence of lip, oral cavity, and pharyngeal cancers by subsite in 2012. CA Cancer J. Clin. 67(1), 51\u201364 (2017)","journal-title":"CA Cancer J. Clin."},{"issue":"11","key":"66_CR23","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.1002\/hed.23189","volume":"35","author":"V Volgger","year":"2013","unstructured":"Volgger, V., et al.: Evaluation of optical coherence tomography to discriminate lesions of the upper aerodigestive tract. Head Neck 35(11), 1558\u20131566 (2013)","journal-title":"Head Neck"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59716-0_66","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T22:04:52Z","timestamp":1759442692000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59716-0_66"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597153","9783030597160"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59716-0_66","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.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":"1809","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":"542","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":"30% - 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 due to the COVID-19 pandemic.","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)"}}]}}