{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T20:08:06Z","timestamp":1777666086579,"version":"3.51.4"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031168512","type":"print"},{"value":"9783031168529","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\/978-3-031-16852-9_1","type":"book-chapter","created":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T19:05:41Z","timestamp":1663614341000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Detecting Melanoma Fairly: Skin Tone Detection and\u00a0Debiasing for\u00a0Skin Lesion Classification"],"prefix":"10.1007","author":[{"given":"Peter J.","family":"Bevan","sequence":"first","affiliation":[]},{"given":"Amir","family":"Atapour-Abarghouei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"1_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1007\/978-3-030-11009-3_34","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"M Alvi","year":"2019","unstructured":"Alvi, M., Zisserman, A., Nell\u00e5ker, C.: Turning a blind eye: explicit removal of biases and variation from deep neural network embeddings. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11129, pp. 556\u2013572. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11009-3_34"},{"key":"1_CR2","unstructured":"Brinker, T.J., et al :A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European J. Cancer (Oxford, England: 1990), 111 148\u2013154 (2019)"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Brinker, T.J., The melanoma classification benchmark, et al.: Comparing artificial intelligence algorithms to 157 German dermatologists. Eur. J. Cancer 111, 30\u201337 (2019)","DOI":"10.1016\/j.ejca.2018.12.016"},{"key":"1_CR4","unstructured":"Buolamwini, J., Gebru, T.: Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Conference on Fairness, Accountability and Transparency, pp. 77\u201391, PMLR (2018)"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Codella, N.C.F., et al.: Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) pp. 168\u2013172 (2018)","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Collins, K.K., Fields, R.C., Baptiste, D., Liu, Y., Moley, J., Jeffe, D.B.: Racial Differences in Survival after Surgical Treatment for Melanoma. Ann. Surg. Oncol. 18(10), 2925\u20132936 (2011)","DOI":"10.1245\/s10434-011-1706-3"},{"issue":"6","key":"1_CR7","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1001\/archderm.1988.01670060015008","volume":"124","author":"TB Fitzpatrick","year":"1988","unstructured":"Fitzpatrick, T.B.: The validity and practicality of sun-reactive skin types I through VI. Arch. Dermatol. 124(6), 869\u2013871 (1988)","journal-title":"Arch. Dermatol."},{"key":"1_CR8","series-title":"Advances in Computer Vision and Pattern Recognition","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-3-319-58347-1_10","volume-title":"Domain Adaptation in Computer Vision Applications","author":"Y Ganin","year":"2017","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. In: Csurka, Gabriela (ed.) Domain Adaptation in Computer Vision Applications. ACVPR, pp. 189\u2013209. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-58347-1_10"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Groh, M., et al.: Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset. arXiv:2104.09957 [cs], April 2021","DOI":"10.1109\/CVPRW53098.2021.00201"},{"issue":"8","key":"1_CR10","doi-asserted-by":"publisher","first-page":"1836","DOI":"10.1093\/annonc\/mdy166","volume":"29","author":"HA Haenssle","year":"2018","unstructured":"Haenssle, H.A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A., Kalloo, A., et al.: Man against machine: Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann. Oncol. 29(8), 1836\u20131842 (2018)","journal-title":"Ann. Oncol."},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Han, S.S., et al: Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. J. Invest. Dermatol. 138(7), 1529\u20131538 (2018)","DOI":"10.1016\/j.jid.2018.01.028"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778, Las Vegas, NV, USA, IEEE (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely Connected Convolutional Networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261\u20132269, Honolulu, HI, IEEE (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"1_CR14","unstructured":"Jiang, L., Huang, D., Liu, M., Yang, W.: Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels, August (2020). arXiv:1911.09781 [cs, stat]"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Kim, B., Kim, H., Kim, K., Kim, S., Kim, J.: Learning Not to Learn: Training Deep Neural Networks With Biased Data. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9004\u20139012, Long Beach, CA, USA, June IEEE (2019)","DOI":"10.1109\/CVPR.2019.00922"},{"key":"1_CR16","unstructured":"Kinyanjui, N.M., et al.: Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets. In Fair ML for Health, page 10, Vancouver, Canada, NeurIPS (2019)"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Lio, P.A., Nghiem, P.: Interactive Atlas of Dermoscopy: 2000, Edra Medical Publishing and New Media. 208 pages. Journal of the American Academy of Dermatology. 50(5), 807\u2013808 (2004)","DOI":"10.1016\/j.jaad.2003.07.029"},{"key":"1_CR18","unstructured":"Paszke, A., et al.: PyTorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, F. dAlch\u00e9-Buc, E. Fox, and R. Garnett, Advances in Neural Information Processing Systems 32, pp. 8024\u20138035 Curran Associates Inc (2019)"},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Rotemberg, V., et al.: A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Scientific Data. 8(1) 34 (2021)","DOI":"10.1038\/s41597-021-00815-z"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the Inception Architecture for Computer Vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818\u20132826, IEEE Computer Society (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"1_CR21","unstructured":"Tan, M., Le, Q.V.: EfficientNet: Rethinking Model Scaling forConvolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning. 97 6105\u20136114 (2019)"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous Deep Transfer Across Domains and Tasks. In 2015 IEEE International Conference on Computer Vision (ICCV). pp. 4068\u20134076 (2015)","DOI":"10.1109\/ICCV.2015.463"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R.B., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated Residual Transformations for Deep Neural Networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.634"}],"container-title":["Lecture Notes in Computer Science","Domain Adaptation and Representation Transfer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16852-9_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:28:46Z","timestamp":1710336526000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16852-9_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031168512","9783031168529"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16852-9_1","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":"15 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Domain Adaptation and Representation Transfer","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":"22 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":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dart2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/dart2022","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":"25","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":"13","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":"52% - 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":"2","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)"}}]}}