Racial Bias in Artificial Intelligence Models for Melanoma Classification
Abstract
The use of artificial intelligence (AI) for skin cancer detection has been the subject of much research and development in recent years. However, recent studies suggest that skin cancer classification algorithms may have racial bias, performing worse on patients with darker skin. In this article, we evaluated the performance of an AI model in classify melanomas across 10 different skin tones, according to the Monk Scale. As a result, it was observed that the models have worse performance in classifying melanomas on darker skin.
References
Arora, G., Dubey, A. K., Jaffery, Z. A., and Rocha, A. (2022). Bag of feature and support vector machine based early diagnosis of skin cancer. Neural Computing and Applications, pages 1–8.
Bissoto, A., Fornaciali, M., Valle, E., and Avila, S. (2019). (de) constructing bias on skin lesion datasets. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0.
Brinker, T. J., Hekler, A., Enk, A. H., Berking, C., Haferkamp, S., Hauschild, A., Weichenthal, M., Klode, J., Schadendorf, D., Holland-Letz, T., et al. (2019). Deep neural networks are superior to dermatologists in melanoma image classification. European Journal of Cancer, 119:11–17.
Bruce, A. and Bruce, P. (2019). Estatística prática para cientistas de dados. Alta Books.
Buolamwini, J. A. (2017). Gender shades: intersectional phenotypic and demographic evaluation of face datasets and gender classifiers. PhD thesis, Massachusetts Institute of Technology.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee.
Gupta, A. K., Bharadwaj, M., and Mehrotra, R. (2016). Skin cancer concerns in people of color: risk factors and prevention. Asian Pacific journal of cancer prevention: APJCP, 17(12):5257.
Gutman, D., Codella, N. C., Celebi, E., Helba, B., Marchetti, M., Mishra, N., and Halpern, A. (2016). Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (isbi) 2016, hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1605.01397.
Hekler, A., Utikal, J. S., Enk, A. H., Hauschild, A., Weichenthal, M., Maron, R. C., Berking, C., Haferkamp, S., Klode, J., Schadendorf, D., et al. (2019). Superior skin cancer classification by the combination of human and artificial intelligence. European Journal of Cancer, 120:114–121.
Jicman, P. A., Smart, H., Ayello, E. A., and Sibbald, R. G. (2023). Early malignant melanoma detection, especially in persons with pigmented skin. Advances in Skin & Wound Care, 36(2):69–77.
Lynn, N. C. and War, N. (2019). Melanoma classification on dermoscopy skin images using bag tree ensemble classifier. In 2019 International Conference on Advanced Information Technologies (ICAIT), pages 120–125. IEEE.
Mendonça, T., Ferreira, P. M., Marques, J. S., Marcal, A. R., and Rozeira, J. (2013). Ph 2-a dermoscopic image database for research and benchmarking. In 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pages 5437–5440. IEEE.
Menegola, A., Fornaciali, M., Pires, R., Bittencourt, F. V., Avila, S., and Valle, E. (2017). Knowledge transfer for melanoma screening with deep learning. In 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), pages 297–300. IEEE.
Nambisan, A. K., Maurya, A., Lama, N., Phan, T., Patel, G., Miller, K., Lama, B., Hagerty, J., Stanley, R., and Stoecker, W. V. (2023). Improving automatic melanoma diagnosis using deep learning-based segmentation of irregular networks. Cancers, 15(4):1259.
Rezk, E., Eltorki, M., El-Dakhakhni, W., et al. (2022). Improving skin color diversity in cancer detection: Deep learning approach. JMIR Dermatology, 5(3):e39143.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
WCRF, W. C. R. F. I. Cancer trends: Skin cancer statistics.
Bissoto, A., Fornaciali, M., Valle, E., and Avila, S. (2019). (de) constructing bias on skin lesion datasets. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0.
Brinker, T. J., Hekler, A., Enk, A. H., Berking, C., Haferkamp, S., Hauschild, A., Weichenthal, M., Klode, J., Schadendorf, D., Holland-Letz, T., et al. (2019). Deep neural networks are superior to dermatologists in melanoma image classification. European Journal of Cancer, 119:11–17.
Bruce, A. and Bruce, P. (2019). Estatística prática para cientistas de dados. Alta Books.
Buolamwini, J. A. (2017). Gender shades: intersectional phenotypic and demographic evaluation of face datasets and gender classifiers. PhD thesis, Massachusetts Institute of Technology.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee.
Gupta, A. K., Bharadwaj, M., and Mehrotra, R. (2016). Skin cancer concerns in people of color: risk factors and prevention. Asian Pacific journal of cancer prevention: APJCP, 17(12):5257.
Gutman, D., Codella, N. C., Celebi, E., Helba, B., Marchetti, M., Mishra, N., and Halpern, A. (2016). Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (isbi) 2016, hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1605.01397.
Hekler, A., Utikal, J. S., Enk, A. H., Hauschild, A., Weichenthal, M., Maron, R. C., Berking, C., Haferkamp, S., Klode, J., Schadendorf, D., et al. (2019). Superior skin cancer classification by the combination of human and artificial intelligence. European Journal of Cancer, 120:114–121.
Jicman, P. A., Smart, H., Ayello, E. A., and Sibbald, R. G. (2023). Early malignant melanoma detection, especially in persons with pigmented skin. Advances in Skin & Wound Care, 36(2):69–77.
Lynn, N. C. and War, N. (2019). Melanoma classification on dermoscopy skin images using bag tree ensemble classifier. In 2019 International Conference on Advanced Information Technologies (ICAIT), pages 120–125. IEEE.
Mendonça, T., Ferreira, P. M., Marques, J. S., Marcal, A. R., and Rozeira, J. (2013). Ph 2-a dermoscopic image database for research and benchmarking. In 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pages 5437–5440. IEEE.
Menegola, A., Fornaciali, M., Pires, R., Bittencourt, F. V., Avila, S., and Valle, E. (2017). Knowledge transfer for melanoma screening with deep learning. In 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), pages 297–300. IEEE.
Nambisan, A. K., Maurya, A., Lama, N., Phan, T., Patel, G., Miller, K., Lama, B., Hagerty, J., Stanley, R., and Stoecker, W. V. (2023). Improving automatic melanoma diagnosis using deep learning-based segmentation of irregular networks. Cancers, 15(4):1259.
Rezk, E., Eltorki, M., El-Dakhakhni, W., et al. (2022). Improving skin color diversity in cancer detection: Deep learning approach. JMIR Dermatology, 5(3):e39143.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
WCRF, W. C. R. F. I. Cancer trends: Skin cancer statistics.
Published
2023-08-06
How to Cite
PAULINO, José Alberto Souza.
Racial Bias in Artificial Intelligence Models for Melanoma Classification. In: WORKSHOP ON THE IMPLICATIONS OF COMPUTING IN SOCIETY (WICS), 4. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 107-114.
ISSN 2763-8707.
DOI: https://doi.org/10.5753/wics.2023.229667.
