{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T20:55:05Z","timestamp":1765486505406,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030598600"},{"type":"electronic","value":"9783030598617"}],"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\/978-3-030-59861-7_23","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T03:07:37Z","timestamp":1601608057000},"page":"220-230","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2834-605X","authenticated-orcid":false,"given":"Kevin","family":"Pan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1052-3564","authenticated-orcid":false,"given":"Guillem","family":"Hurault","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0459-892X","authenticated-orcid":false,"given":"Kai","family":"Arulkumaran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5646-3093","authenticated-orcid":false,"given":"Hywel C.","family":"Williams","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0769-9382","authenticated-orcid":false,"given":"Reiko J.","family":"Tanaka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"23_CR1","unstructured":"Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: USENIX Symposium on Operating Systems Design and Implementation, pp. 265\u2013283 (2016)"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Alam, M.N., Munia, T.T.K., Tavakolian, K., Vasefi, F., MacKinnon, N., Fazel-Rezai, R.: Automatic detection and severity measurement of eczema using image processing. In: EMBC, pp. 1365\u20131368. IEEE (2016)","DOI":"10.1109\/EMBC.2016.7590961"},{"key":"23_CR3","unstructured":"Ashukha, A., Lyzhov, A., Molchanov, D., Vetrov, D.: Pitfalls of in-domain uncertainty estimation and ensembling in deep learning. In: International Conference on Learning Representations (2020)"},{"key":"23_CR4","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1111\/j.1365-2133.1996.tb00706.x","volume":"135","author":"J Berth-Jones","year":"1996","unstructured":"Berth-Jones, J.: Six area, six sign atopic dermatitis (SASSAD) severity score: a simple system for monitoring disease activity in atopic dermatitis. Br. J. Dermatol. 135, 25\u201330 (1996)","journal-title":"Br. J. Dermatol."},{"key":"23_CR5","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ejca.2019.04.001","volume":"113","author":"TJ Brinker","year":"2019","unstructured":"Brinker, T.J., et al.: Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur. J. Cancer 113, 47\u201354 (2019)","journal-title":"Eur. J. Cancer"},{"key":"23_CR6","unstructured":"Cardoso, J.S., Costa, J.F.: Learning to classify ordinal data: the data replication method. JMLR 8(Jul), 1393\u20131429 (2007)"},{"issue":"7639","key":"23_CR7","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(7639), 115\u2013118 (2017)","journal-title":"Nature"},{"key":"23_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/3-540-44795-4_13","volume-title":"Machine Learning: ECML 2001","author":"E Frank","year":"2001","unstructured":"Frank, E., Hall, M.: A simple approach to ordinal classification. In: De Raedt, L., Flach, P. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 145\u2013156. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-44795-4_13"},{"key":"23_CR9","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: ICML, pp. 1321\u20131330 (2017)"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Hameed, N., Shabut, A.M., Hossain, M.A.: Multi-class skin diseases classification using deep convolutional neural network and support vector machine. In: SKIMA, pp. 1\u20137. IEEE (2018)","DOI":"10.1109\/SKIMA.2018.8631525"},{"issue":"1","key":"23_CR11","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1034\/j.1600-0625.2001.100102.x","volume":"10","author":"J Hanifin","year":"2001","unstructured":"Hanifin, J., et al.: The eczema area and severity index (EASI): assessment of reliability in atopic dermatitis. Exp. Dermatol. 10(1), 11\u201318 (2001)","journal-title":"Exp. Dermatol."},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR13","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861 (2017)"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Huang, J., et al.: Speed\/accuracy trade-offs for modern convolutional object detectors. In: CVPR, pp. 7310\u20137311 (2017)","DOI":"10.1109\/CVPR.2017.351"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Padilla, D., Yumang, A., Diaz, A.L., Inlong, G.: Differentiating atopic dermatitis and psoriasis chronic plaque using convolutional neural network mobilenet architecture. In: HNICEM, pp. 1\u20136 (2019)","DOI":"10.1109\/HNICEM48295.2019.9073482"},{"key":"23_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1007\/978-3-030-01201-4_27","volume-title":"OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis","author":"A Pal","year":"2018","unstructured":"Pal, A., Chaturvedi, A., Garain, U., Chandra, A., Chatterjee, R., Senapati, S.: Severity assessment of psoriatic plaques using deep CNN based ordinal classification. In: Stoyanov, D., et al. (eds.) CARE\/CLIP\/OR 2.0\/ISIC -2018. LNCS, vol. 11041, pp. 252\u2013259. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01201-4_27"},{"key":"23_CR17","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NeurIPS, pp. 91\u201399 (2015)"},{"issue":"6","key":"23_CR18","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1016\/j.jaci.2013.07.008","volume":"132","author":"J Schmitt","year":"2013","unstructured":"Schmitt, J., et al.: Assessment of clinical signs of atopic dermatitis: a systematic review and recommendation. J. Allergy Clin. Immunol. 132(6), 1337\u20131347 (2013)","journal-title":"J. Allergy Clin. Immunol."},{"issue":"4","key":"23_CR19","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1016\/j.jaci.2014.07.043","volume":"134","author":"J Schmitt","year":"2014","unstructured":"Schmitt, J., et al.: The Harmonising Outcome Measures for Eczema (HOME) statement to assess clinical signs of atopic eczema in trials. J. Allergy Clin. Immunol. 134(4), 800\u2013807 (2014)","journal-title":"J. Allergy Clin. Immunol."},{"key":"23_CR20","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)"},{"issue":"1","key":"23_CR21","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. JMLR 15(1), 1929\u20131958 (2014)","journal-title":"JMLR"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"issue":"8","key":"23_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3310\/hta15080","volume":"15","author":"K Thomas","year":"2011","unstructured":"Thomas, K., et al.: A multicentre randomised controlled trial and economic evaluation of ion-exchange water softeners for the treatment of eczema in children: the softened water eczema trial (SWET). Health Technol. Assess. 15(8), 1\u2013156 (2011)","journal-title":"Health Technol. Assess."},{"key":"23_CR24","doi-asserted-by":"publisher","first-page":"180161","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018)","journal-title":"Sci. Data"},{"issue":"1","key":"23_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41572-018-0001-z","volume":"4","author":"S Weidinger","year":"2018","unstructured":"Weidinger, S., Beck, L.A., Bieber, T., Kabashima, K., Irvine, A.D.: Atopic dermatitis. Nat. Rev. Dis. Primers 4(1), 1 (2018)","journal-title":"Nat. Rev. Dis. Primers"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"Wolkerstorfer, A., De Waard van der Spek, F., Glazenburg, E., Mulder, P., Oranje, A.: Scoring the severity of atopic dermatitis: three item severity score as a rough system for daily practice and as a pre-screening tool for studies. Acta Dermato-Venereologica 79, 356\u2013359 (1999)","DOI":"10.1080\/000155599750010256"},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Wu, H., et al.: A deep learning, image based approach for automated diagnosis for inflammatory skin diseases. Ann. Transl. Med. 8(9) (2020)","DOI":"10.21037\/atm.2020.04.39"},{"key":"23_CR28","unstructured":"Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: NeurIPS, pp. 3320\u20133328 (2014)"},{"issue":"1","key":"23_CR29","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.jaci.2006.02.031","volume":"118","author":"T Zuberbier","year":"2006","unstructured":"Zuberbier, T., et al.: Patient perspectives on the management of atopic dermatitis. J. Allergy Clin. Immunol. 118(1), 226\u2013232 (2006)","journal-title":"J. Allergy Clin. Immunol."}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59861-7_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:10:33Z","timestamp":1759356633000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59861-7_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030598600","9783030598617"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59861-7_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","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":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mlmi2020.web.unc.edu\/","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":"101","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":"68","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":"67% - 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":"2.04","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":"3.43","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)"}}]}}