Dildar, M., Akram, S., Irfan, M., Khan, H.U., Ramzan, M., Mahmood, A.R., Alsaiari, S.A., Saeed, A.H.M., Alraddadi, M.O., Mahnashi, M.H.: Skin cancer detection: a review using deep learning techniques. International journal of environmental research and public health 18(10), 5479 (2021)
Wu, Y., Chen, B., Zeng, A., Pan, D., Wang, R., Zhao, S.: Skin cancer classification with deep learning: a systematic review. Frontiers in Oncology 12, 893972 (2022)
Nguyen, A.T.P., Jewel, R.M., Akter, A.: Comparative analysis of machine learning models for automated skin cancer detection: Advancements in diagnostic accuracy and ai integration. The American Journal of Medical Sciences and Pharmaceutical Research 7(01), 15–26 (2025)
Bhardwaj, A., Rege, P.P.: Skin lesion classification using deep learning. In: Advances in Signal and Data Processing: Select Proceedings of ICSDP 2019, pp. 575–589 (2021). Springer
Adegun, A., Viriri, S.: Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art. Artificial Intelligence Review 54(2), 811–841 (2021)
Zhang, Y., Jiang, H., Miura, Y., Manning, C.D., Langlotz, C.P.: Contrastive learning of medical visual representations from paired images and text. In: Machine Learning for Healthcare Conference, pp. 2–25 (2022). PMLR
Abhari, J., Ashok, A.: Mitigating racial biases for machine learning based skin cancer detection. In: Proceedings of the Twenty-Fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, pp. 556–561 (2023)
Seth, P., Pai, A.K.: Does the fairness of your pre-training hold up? examining the influence of pre-training techniques on skin tone bias in skin lesion classification. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 570–577 (2024)
Chiu, C.-H., Chen, Y.-J., Wu, Y., Shi, Y., Ho, T.-Y.: Achieve fairness without demographics for dermatological disease diagnosis. Medical Image Analysis 95, 103188 (2024)
Wu, Y., Zeng, D., Xu, X., Shi, Y., Hu, J.: Fairprune: Achieving fairness through pruning for dermatological disease diagnosis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 743–753 (2022). Springer
Xu, Z., Zhao, S., Quan, Q., Yao, Q., Zhou, S.K.: Fairadabn: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 307–317 (2023). Springer
Du, S., Hers, B., Bayasi, N., Hamarneh, G., Garbi, R.: Fairdisco: Fairer ai in dermatology via disentanglement contrastive learning. In: European Conference on Computer Vision, pp. 185–202 (2022). Springer
Aayushman, Gaddey, H., Mittal, V., Chawla, M., Gupta, G.R.: Fair and accurate skin disease image classification by alignment with clinical labels. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 394–404 (2024). Springer
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PmLR
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised contrastive learning. Advances in neural information processing systems 33, 18661–18673 (2020)
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763 (2021). PmLR
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q., Sung, Y.-H., Li, Z., Duerig, T.: Scaling up visual and vision-language representation learning with noisy text supervision. In: International Conference on Machine Learning, pp. 4904–4916 (2021). PMLR
Park, S., Lee, J., Lee, P., Hwang, S., Kim, D., Byun, H.: Fair contrastive learning for facial attribute classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10389–10398 (2022)
Thong, W., Joniak, P., Xiang, A.: Beyond skin tone: A multidimensional measure of apparent skin color. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4903–4913 (2023)
Harvey, V.M., Alexis, A., Okeke, C.A., McKinley-Grant, L., Taylor, S.C., Desai, S.R., Jaleel, T., Heath, C.R., Kang, S., Vashi, N., et al.: Integrating skin color assessments into clinical practice and research: a review of current approaches. Journal of the American Academy of Dermatology (2024)
Cohen, P.R., DiMarco, M.A., Geller, R.L., Darrisaw, L.A., Geller, R., Darrisaw, L.: Colorimetric scale for skin of color: a practical classification scale for the clinical assessment, dermatology management, and forensic evaluation of individuals with skin of color. Cureus 15(11) (2023)
Pusey-Reid, E., Quinn, L., Samost, M.E., Reidy, P.A.: Skin assessment in patients with dark skin tone. AJN The American Journal of Nursing 123(3), 36–43 (2023)
Gupta, V., Sharma, V.K.: Skin typing: Fitzpatrick grading and others. Clinics in dermatology 37(5), 430–436 (2019)
Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)
Pacheco, A.G., Lima, G.R., Salomao, A.S., Krohling, B., Biral, I.P., Angelo, G.G., Alves Jr, F.C., Esgario, J.G., Simora, A.C., Castro, P.B., et al.: Pad-ufes-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones. Data in brief 32, 106221 (2020)
Groh, M., Harris, C., Soenksen, L., Lau, F., Han, R., Kim, A., Koochek, A., Badri, O.: 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. 1820–1828 (2021)
Sachdeva, S.: Fitzpatrick skin typing: Applications in dermatology. Indian journal of dermatology, venereology and leprology 75, 93 (2009)
Du, M., Yang, F., Zou, N., Hu, X.: Fairness in deep learning: A computational perspective. IEEE Intelligent Systems 36(4), 25–34 (2020)
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. 770–778 (2016)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (long and Short Papers), pp. 4171–4186 (2019)
Alsentzer, E., Murphy, J.R., Boag, W., Weng, W.-H., Jin, D., Naumann, T., Redmond, W., McDermott, M.B.: Publicly available clinical bert embeddings. NAACL HLT 2019, 72 (2019)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowledge and information systems 33(1), 1–33 (2012)
Xu, T., White, J., Kalkan, S., Gunes, H.: Investigating bias and fairness in facial expression recognition. In: Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part VI 16, pp. 506–523 (2020). Springer
Comments (0)