{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T17:58:58Z","timestamp":1792087138931,"version":"4.4.0"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164484","type":"print"},{"value":"9783031164491","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\/978-3-031-16449-1_29","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T04:04:54Z","timestamp":1663301094000},"page":"298-308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Deep Multimodal Guidance for\u00a0Medical Image Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9432-4262","authenticated-orcid":false,"given":"Mayur","family":"Mallya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5040-7448","authenticated-orcid":false,"given":"Ghassan","family":"Hamarneh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,17]]},"reference":[{"issue":"1","key":"29_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-87064-7","volume":"11","author":"K Abhishek","year":"2021","unstructured":"Abhishek, K., Kawahara, J., Hamarneh, G.: Predicting the clinical management of skin lesions using deep learning. Sci. Rep. 11(1), 1\u201314 (2021)","journal-title":"Sci. Rep."},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Afouras, T., Chung, J.S., Zisserman, A.: ASR is all you need: cross-modal distillation for lip reading. In: IEEE ICASSP, pp. 2143\u20132147 (2020)","DOI":"10.1109\/ICASSP40776.2020.9054253"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Albanie, S., Nagrani, A., Vedaldi, A., Zisserman, A.: Emotion recognition in speech using cross-modal transfer in the wild. In: ACM International conference on Multimedia, pp. 292\u2013301 (2018)","DOI":"10.1145\/3240508.3240578"},{"issue":"6","key":"29_CR4","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1007\/s00401-015-1432-1","volume":"129","author":"K Aldape","year":"2015","unstructured":"Aldape, K., Zadeh, G., Mansouri, S., Reifenberger, G., von Deimling, A.: Glioblastoma: pathology, molecular mechanisms and markers. Acta Neuropathol. 129(6), 829\u2013848 (2015). https:\/\/doi.org\/10.1007\/s00401-015-1432-1","journal-title":"Acta Neuropathol."},{"issue":"12","key":"29_CR5","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1001\/archderm.134.12.1563","volume":"134","author":"G Argenziano","year":"1998","unstructured":"Argenziano, G., Fabbrocini, G., Carli, P., De Giorgi, V., Sammarco, E., Delfino, M.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions: comparison of the abcd rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Arch. Dermatol. 134(12), 1563\u20131570 (1998)","journal-title":"Arch. Dermatol."},{"issue":"2","key":"29_CR6","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltru\u0161aitis","year":"2018","unstructured":"Baltru\u0161aitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: a survey and taxonomy. IEEE TPAMI 41(2), 423\u2013443 (2018)","journal-title":"IEEE TPAMI"},{"key":"29_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00371-021-02166-7","volume":"38","author":"K Bayoudh","year":"2021","unstructured":"Bayoudh, K., Knani, R., Hamdaoui, F., Mtibaa, A.: A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets. Vis. Comput. 38, 1\u201332 (2021). https:\/\/doi.org\/10.1007\/s00371-021-02166-7","journal-title":"Vis. Comput."},{"issue":"8","key":"29_CR8","first-page":"1369","volume":"41","author":"T Beyer","year":"2000","unstructured":"Beyer, T., et al.: A combined PET\/CT scanner for clinical oncology. J. Nucl. Med. 41(8), 1369\u20131379 (2000)","journal-title":"J. Nucl. Med."},{"key":"29_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1007\/978-3-030-87240-3_64","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"N Braman","year":"2021","unstructured":"Braman, N., Gordon, J.W.H., Goossens, E.T., Willis, C., Stumpe, M.C., Venkataraman, J.: Deep orthogonal fusion: multimodal prognostic biomarker discovery integrating radiology, pathology, genomic, and clinical data. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 667\u2013677. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_64"},{"key":"29_CR10","unstructured":"Chen, R.J., et al.: Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. In: IEEE TMI (2020)"},{"issue":"7","key":"29_CR11","first-page":"2415","volume":"39","author":"Q Dou","year":"2020","unstructured":"Dou, Q., Liu, Q., Heng, P.A., Glocker, B.: Unpaired multi-modal segmentation via knowledge distillation. IEEE TMI 39(7), 2415\u20132425 (2020)","journal-title":"IEEE TMI"},{"issue":"5","key":"29_CR12","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1162\/neco_a_01273","volume":"32","author":"J Gao","year":"2020","unstructured":"Gao, J., Li, P., Chen, Z., Zhang, J.: A survey on deep learning for multimodal data fusion. Neural Comput. 32(5), 829\u2013864 (2020)","journal-title":"Neural Comput."},{"key":"29_CR13","unstructured":"Grandini, M., Bagli, E., Visani, G.: Metrics for multi-class classification: an overview. arXiv preprint arXiv:2008.05756 (2020)"},{"key":"29_CR14","doi-asserted-by":"publisher","first-page":"63373","DOI":"10.1109\/ACCESS.2019.2916887","volume":"7","author":"W Guo","year":"2019","unstructured":"Guo, W., Wang, J., Wang, S.: Deep multimodal representation learning: a survey. IEEE Access 7, 63373\u201363394 (2019)","journal-title":"IEEE Access"},{"issue":"2","key":"29_CR15","first-page":"162","volume":"3","author":"Z Guo","year":"2019","unstructured":"Guo, Z., Li, X., Huang, H., Guo, N., Li, Q.: Deep learning-based image segmentation on multimodal medical imaging. IEEE TRPMS 3(2), 162\u2013169 (2019)","journal-title":"IEEE TRPMS"},{"key":"29_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1007\/978-3-030-59710-8_75","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"M Hu","year":"2020","unstructured":"Hu, M., et al.: Knowledge distillation from multi-modal to mono-modal segmentation networks. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 772\u2013781. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_75"},{"issue":"2","key":"29_CR17","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 J. Biomed. Health Inf. 23(2), 538\u2013546 (2018)","journal-title":"IEEE J. Biomed. Health Inf."},{"issue":"1","key":"29_CR18","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/JBHI.2016.2635663","volume":"21","author":"A Kumar","year":"2016","unstructured":"Kumar, A., Kim, J., Lyndon, D., Fulham, M., Feng, D.: An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE J. Biomed. Health Inf. 21(1), 31\u201340 (2016)","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"29_CR19","doi-asserted-by":"publisher","first-page":"27","DOI":"10.3389\/fnins.2020.00027","volume":"14","author":"T Kurc","year":"2020","unstructured":"Kurc, T., et al.: Segmentation and classification in digital pathology for glioma research: challenges and deep learning approaches. Front. Neurosci. 14, 27 (2020)","journal-title":"Front. Neurosci."},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Li, K., Yu, L., Wang, S., Heng, P.A.: Towards cross-modality medical image segmentation with online mutual knowledge distillation. In: AAAI, vol. 34, pp. 775\u2013783 (2020)","DOI":"10.1609\/aaai.v34i01.5421"},{"issue":"6","key":"29_CR21","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1016\/j.eururo.2013.05.049","volume":"64","author":"S Loeb","year":"2013","unstructured":"Loeb, S., et al.: Systematic review of complications of prostate biopsy. Eur. Urol. 64(6), 876\u2013892 (2013)","journal-title":"Eur. Urol."},{"issue":"6","key":"29_CR22","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu, M.Y., Williamson, D.F., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5(6), 555\u2013570 (2021)","journal-title":"Nat. Biomed. Eng."},{"key":"29_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/978-3-030-46643-5_34","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"X Ma","year":"2020","unstructured":"Ma, X., Jia, F.: Brain tumor classification with multimodal MR and pathology images. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11993, pp. 343\u2013352. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46643-5_34"},{"issue":"13","key":"29_CR24","doi-asserted-by":"publisher","first-page":"E2970","DOI":"10.1073\/pnas.1717139115","volume":"115","author":"P Mobadersany","year":"2018","unstructured":"Mobadersany, P., et al.: Predicting cancer outcomes from histology and genomics using convolutional networks. Proc. Nat. Acad. Sci. 115(13), E2970\u2013E2979 (2018)","journal-title":"Proc. Nat. Acad. Sci."},{"key":"29_CR25","doi-asserted-by":"crossref","unstructured":"Nagrani, A., Albanie, S., Zisserman, A.: Seeing voices and hearing faces: cross-modal biometric matching. In: IEEE CVPR, pp. 8427\u20138436 (2018)","DOI":"10.1109\/CVPR.2018.00879"},{"issue":"2","key":"29_CR26","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1002\/path.4282","volume":"232","author":"A Olar","year":"2014","unstructured":"Olar, A., Aldape, K.D.: Using the molecular classification of glioblastoma to inform personalized treatment. J. Pathol. 232(2), 165\u2013177 (2014)","journal-title":"J. Pathol."},{"issue":"1","key":"29_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-74419-9","volume":"10","author":"L Pei","year":"2020","unstructured":"Pei, L., Vidyaratne, L., Rahman, M.M., Iftekharuddin, K.M.: Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images. Sci. Rep. 10(1), 1\u201311 (2020)","journal-title":"Sci. Rep."},{"issue":"4","key":"29_CR28","doi-asserted-by":"publisher","first-page":"1828","DOI":"10.1007\/s00330-019-06523-y","volume":"30","author":"MM Rehani","year":"2020","unstructured":"Rehani, M.M., et al.: Patients undergoing recurrent CT scans: assessing the magnitude. Eur. Radiol. 30(4), 1828\u20131836 (2020)","journal-title":"Eur. Radiol."},{"issue":"10","key":"29_CR29","doi-asserted-by":"publisher","first-page":"1965","DOI":"10.1088\/0031-9155\/42\/10\/010","volume":"42","author":"Y Shao","year":"1997","unstructured":"Shao, Y., et al.: Simultaneous PET and MR imaging. Phys. Med. Biol. 42(10), 1965 (1997)","journal-title":"Phys. Med. Biol."},{"key":"29_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1007\/978-3-030-78191-0_26","volume-title":"Information Processing in Medical Imaging","author":"T van Sonsbeek","year":"2021","unstructured":"van Sonsbeek, T., Zhen, X., Worring, M., Shao, L.: Variational knowledge distillation for disease classification in chest X-rays. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds.) IPMI 2021. LNCS, vol. 12729, pp. 334\u2013345. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78191-0_26"},{"key":"29_CR31","doi-asserted-by":"publisher","first-page":"63514","DOI":"10.1109\/ACCESS.2020.2982224","volume":"8","author":"L Wang","year":"2020","unstructured":"Wang, L., Chen, W., Yang, W., Bi, F., Yu, F.R.: A state-of-the-art review on image synthesis with generative adversarial networks. IEEE Access 8, 63514\u201363537 (2020)","journal-title":"IEEE Access"},{"key":"29_CR32","unstructured":"Wang, L., Yoon, K.J.: Knowledge distillation and student-teacher learning for visual intelligence: a review and new outlooks. In: IEEE TPAMI (2021)"},{"issue":"1","key":"29_CR33","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1002\/acm2.13121","volume":"22","author":"T Wang","year":"2021","unstructured":"Wang, T., et al.: A review on medical imaging synthesis using deep learning and its clinical applications. J. Appl. Clin. Med. Phys. 22(1), 11\u201336 (2021)","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"29_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-3-030-32962-4_18","volume-title":"Health Information Science","author":"Y Xu","year":"2019","unstructured":"Xu, Y.: Deep learning in multimodal medical image analysis. In: Wang, H., et al. (eds.) HIS 2019. LNCS, vol. 11837, pp. 193\u2013200. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32962-4_18"},{"key":"29_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.104042","volume":"105","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Sidib\u00e9, D., Morel, O., M\u00e9riaudeau, F.: Deep multimodal fusion for semantic image segmentation: a survey. Image Vis. Comput. 105, 104042 (2021)","journal-title":"Image Vis. Comput."}],"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\/978-3-031-16449-1_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:54:59Z","timestamp":1709812499000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16449-1_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164484","9783031164491"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16449-1_29","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":"17 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)"}}]}}