{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T01:43:34Z","timestamp":1773107014539,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031776090","type":"print"},{"value":"9783031776106","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-77610-6_2","type":"book-chapter","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T04:09:40Z","timestamp":1737000580000},"page":"14-23","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["From Majority to\u00a0Minority: A Diffusion-Based Augmentation for\u00a0Underrepresented Groups in\u00a0Skin Lesion Analysis"],"prefix":"10.1007","author":[{"given":"Janet","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yunsung","family":"Chung","sequence":"additional","affiliation":[]},{"given":"Zhengming","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Jihun","family":"Hamm","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Akrout, M., et al.: Diffusion-based data augmentation for skin disease classification: impact across original medical datasets to fully synthetic images (2023)","DOI":"10.1007\/978-3-031-53767-7_10"},{"key":"2_CR2","doi-asserted-by":"publisher","unstructured":"Brinker, T., et al.: A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. Eur. J. Cancer 111, 148\u2013154 (2019). https:\/\/doi.org\/10.1016\/j.ejca.2019.02.005","DOI":"10.1016\/j.ejca.2019.02.005"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Coustasse, A., Sarkar, R., Abodunde, B., Metzger, B.J., Slater, C.M.: Use of teledermatology to improve dermatological access in rural areas. Telemedicine Journal and e-Health: The Official Journal of the American Telemedicine Association (2019)","DOI":"10.1089\/tmj.2018.0130"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Daneshjou, R., et al.: Disparities in dermatology AI performance on a diverse, curated clinical image set. Science Advances (2022)","DOI":"10.1126\/sciadv.abq6147"},{"key":"2_CR5","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780\u20138794 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2_CR6","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":"6","key":"2_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":"2_CR8","unstructured":"Gal, R., et al.: An image is worth one word: personalizing text-to-image generation using textual inversion. In: The Eleventh International Conference on Learning Representations (2023). https:\/\/openreview.net\/forum?id=NAQvF08TcyG"},{"key":"2_CR9","unstructured":"Ghorbani, A., Natarajan, V., Coz, D., Liu, Y.: DermGAN: synthetic generation of clinical skin images with pathology. In: Dalca, A.V., et al. (eds.) Proceedings of the Machine Learning for Health NeurIPS Workshop. Proceedings of Machine Learning Research, vol.\u00a0116, pp. 155\u2013170. PMLR (2020). https:\/\/proceedings.mlr.press\/v116\/ghorbani20a.html"},{"issue":"11","key":"2_CR10","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Groh, M., et al.: Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1820\u20131828 (2021)","DOI":"10.1109\/CVPRW53098.2021.00201"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2_CR13","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2_CR14","unstructured":"Hu, E.J., et al.: LoRA: low-rank adaptation of large language models. In: International Conference on Learning Representations (2022). https:\/\/openreview.net\/forum?id=nZeVKeeFYf9"},{"key":"2_CR15","unstructured":"Ktena, I., et al.: Generative models improve fairness of medical classifiers under distribution shifts (2023)"},{"issue":"6","key":"2_CR16","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1038\/s41591-020-0842-3","volume":"26","author":"Y Liu","year":"2020","unstructured":"Liu, Y., et al.: A deep learning system for differential diagnosis of skin diseases. Nat. Med. 26(6), 900\u2013908 (2020)","journal-title":"Nat. Med."},{"key":"2_CR17","unstructured":"von Platen, P., et al.: Diffusers: state-of-the-art diffusion models. https:\/\/github.com\/huggingface\/diffusers (2022)"},{"key":"2_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105568","volume":"195","author":"Z Qin","year":"2020","unstructured":"Qin, Z., Liu, Z., Zhu, P., Xue, Y.: A GAN-based image synthesis method for skin lesion classification. Comput. Methods Programs Biomed. 195, 105568 (2020)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"3","key":"2_CR19","doi-asserted-by":"publisher","DOI":"10.2196\/39143","volume":"5","author":"E Rezk","year":"2022","unstructured":"Rezk, E., Eltorki, M., El-Dakhakhni, W., et al.: Improving skin color diversity in cancer detection: deep learning approach. JMIR Dermatol. 5(3), e39143 (2022)","journal-title":"JMIR Dermatol."},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"2_CR21","unstructured":"Sagers, L.W., et al.: Augmenting medical image classifiers with synthetic data from latent diffusion models (2023)"},{"key":"2_CR22","unstructured":"Sagers, L.W., Diao, J.A., Groh, M., Rajpurkar, P., Adamson, A., Manrai, A.K.: Improving dermatology classifiers across populations using images generated by large diffusion models. In: NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research (2022). https:\/\/openreview.net\/forum?id=Vzdbjtz6Tys"},{"key":"2_CR23","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, Y., Ding, Z., Hamm, J.: Achieving reliable and fair skin lesion diagnosis via unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5157\u20135166 (2024)","DOI":"10.1109\/CVPRW63382.2024.00523"},{"key":"2_CR25","unstructured":"Wu, B., et al.: Visual transformers: token-based image representation and processing for computer vision (2020)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-77610-6_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T17:27:51Z","timestamp":1746725271000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-77610-6_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031776090","9783031776106"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-77610-6_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"17 January 2025","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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}