{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T20:39:13Z","timestamp":1760647153621,"version":"3.37.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030393427"},{"type":"electronic","value":"9783030393434"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-39343-4_8","type":"book-chapter","created":{"date-parts":[[2020,1,23]],"date-time":"2020-01-23T06:03:29Z","timestamp":1579759409000},"page":"86-98","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Classification of Ten Skin Lesion Classes: Hierarchical KNN versus Deep Net"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6860-9371","authenticated-orcid":false,"given":"Robert B.","family":"Fisher","sequence":"first","affiliation":[]},{"given":"Jonathan","family":"Rees","sequence":"additional","affiliation":[]},{"given":"Antoine","family":"Bertrand","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,24]]},"reference":[{"key":"8_CR1","unstructured":"American Cancer Society. Cancer Facts & Figures (2016)"},{"key":"8_CR2","series-title":"Lecture Notes in Computer Vision and Biomechanics","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-007-5389-1_4","volume-title":"Color Medical Image Analysis","author":"L Ballerini","year":"2013","unstructured":"Ballerini, L., Fisher, R.B., Aldridge, R.B., Rees, J.: A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions. In: Celebi, M.E., Schaefer, G. (eds.) Color Medical Image Analysis. Lecture Notes in Computer Vision and Biomechanics, vol. 6. Springer, Dordrecht (2013). \nhttps:\/\/doi.org\/10.1007\/978-94-007-5389-1_4"},{"key":"8_CR3","unstructured":"Bertrand, A.: Classification of skin lesions images using deep nets. Intern report, INP Grenoble (2018)"},{"key":"8_CR4","unstructured":"Di Leo, C., Bevilacqua, V., Ballerini, L., Fisher, R., Aldridge, B., Rees, J.: Hierarchical classification of ten skin lesion classes. In: Proceedings of Medical Image Analysis Workshop, Dundee (2015)"},{"key":"8_CR5","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118 (2017)","journal-title":"Nature"},{"issue":"5","key":"8_CR6","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1016\/j.jaad.2015.07.028","volume":"73","author":"LK Ferris","year":"2015","unstructured":"Ferris, L.K., et al.: Computer-aided classification of melanocytic lesions using dermoscopic images. J. Am. Acad. Dermatol. 73(5), 769\u2013776 (2015)","journal-title":"J. Am. Acad. Dermatol."},{"issue":"6","key":"8_CR7","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"3","author":"RM Haralick","year":"1973","unstructured":"Haralick, R.M., Shanmungam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. B Cybern. 3(6), 610\u2013621 (1973)","journal-title":"IEEE Trans. Syst. Man Cybern. B Cybern."},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Kawahara, J., BenTaieb, A., Hamarneh, G.: Deep features to classify skin lesions. In: Proceedings of IEEE 13th International Symposium on Biomedical Imaging (ISBI) (2016)","DOI":"10.1109\/ISBI.2016.7493528"},{"key":"8_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1007\/978-3-319-47157-0_20","volume-title":"Machine Learning in Medical Imaging","author":"J Kawahara","year":"2016","unstructured":"Kawahara, J., Hamarneh, G.: Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, H.-I. (eds.) MLMI 2016. LNCS, vol. 10019, pp. 164\u2013171. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-47157-0_20"},{"issue":"2","key":"8_CR11","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.artmed.2012.08.002","volume":"56","author":"K Korotkov","year":"2012","unstructured":"Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: a review. Artif. Intell. Med. 56(2), 69\u201390 (2012)","journal-title":"Artif. Intell. Med."},{"key":"8_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1007\/978-3-642-04271-3_133","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2009","author":"X Li","year":"2009","unstructured":"Li, X., Aldridge, B., Ballerini, L., Fisher, R., Rees, J.: Depth data improves skin lesion segmentation. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 1100\u20131107. Springer, Heidelberg (2009). \nhttps:\/\/doi.org\/10.1007\/978-3-642-04271-3_133"},{"issue":"5","key":"8_CR13","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1109\/TITB.2009.2017529","volume":"13","author":"I Maglogiannis","year":"2009","unstructured":"Maglogiannis, I., Doukas, C.N.: Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans. Inf. Technol. Biomed. 13(5), 721\u2013733 (2009)","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"8_CR14","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1155\/2013\/323268","volume":"2013","author":"A Masood","year":"2013","unstructured":"Masood, A., Al-Jumaily, A.A.: Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int. J. Biomed. Imaging 2013, 22 (2013)","journal-title":"Int. J. Biomed. Imaging"},{"key":"8_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/978-3-030-01201-4_33","volume-title":"OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures,and Skin Image Analysis","author":"F Perez","year":"2018","unstructured":"Perez, F., Vasconcelos, C., Avila, S., Valle, E.: Data augmentation for skin lesion analysis. In: Stoyanov, D., et al. (eds.) CARE\/CLIP\/OR 2.0\/ISIC -2018. LNCS, vol. 11041, pp. 303\u2013311. Springer, Cham (2018). \nhttps:\/\/doi.org\/10.1007\/978-3-030-01201-4_33"}],"container-title":["Communications in Computer and Information Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-39343-4_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,23]],"date-time":"2020-01-23T06:14:53Z","timestamp":1579760093000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-39343-4_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030393427","9783030393434"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-39343-4_8","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"24 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Liverpool","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 July 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2019","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":"miua2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miua2019.com\/","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":"ocs","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"70","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":"43","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":"61% - 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":"2","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":"3","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)"}}]}}