Evaluating Subgroup Fairness in Deep Learning Models for Breast Cancer Tumor Segmentation
Resumo
Machine learning is widely utilized across diverse fields, including healthcare. In this context, issues such as privacy and model fairness have been gaining increasing importance. It is not sufficient for a model to achieve good overall performance: it must be effective for all subgroups comprising the target population. This study investigates breast cancer tumor segmentation models using DCE-MRI from a fairness perspective. The experiments were conducted using the MAMA-MIA dataset and the nnU-Net framework, which represents the state-of-the-art in several medical image segmentation tasks. Model performances were compared through a fairness lens. In the investigated models, performance discrepancies were found across all evaluation metrics used.Referências
Abdelwanis, M., Moawad, K., Mohammed, S., Hummieda, A., Syed, S., Maalouf, M., and Jelinek, H. F. (2025). Sequential classification approach for enhancing the assessment of cardiac autonomic neuropathy. Computers in Biology and Medicine, 190:109999.
Barocas, S. and Selbst, A. D. (2016). Big Data’s Disparate Impact. California Law Review, 104(3):671–732.
Bitencourt, A. G. V. and Chojniak, R. (2021). Indications for breast magnetic resonance imaging in Brazil: Past, present, and future. Radiologia Brasileira, 54:206–206.
Buolamwini, J. and Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency, pages 77–91. PMLR.
Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., and Mahmood, F. (2023). Algorithmic fairness in artificial intelligence for medicine and healthcare. Nature Biomedical Engineering, 7(6):719–742.
Costa, L. D. L. N., Sardinha, A. H. d. L., Verzaro, P. M., Lisbôa, L. L. C., and Batista, R. F. L. (2019). Mortalidade por Câncer de Mama e Condições de Desenvolvimento Humano no Brasil. Revista Brasileira de Cancerologia, 65(1):e–12050.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, ITCS ’12, pages 214–226. Association for Computing Machinery.
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., and Socher, R. (2021). Deep learning-enabled medical computer vision. npj Digital Medicine, 4(1):1–9.
Faiyazuddin, Md., Rahman, S. J. Q., Anand, G., Siddiqui, R. K., Mehta, R., Khatib, M. N., Gaidhane, S., Zahiruddin, Q. S., Hussain, A., and Sah, R. (2025). The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency. Health Science Reports, 8(1):e70312.
Garrucho, L., Kushibar, K., Reidel, C.-A., Joshi, S., Osuala, R., Tsirikoglou, A., Bobowicz, M., Riego, J. d., Catanese, A., Gwoździewicz, K., Cosaka, M.-L., Abo-Elhoda, P. M., Tantawy, S. W., Sakrana, S. S., Shawky-Abdelfatah, N. O., Salem, A. M. A., Kozana, A., Divjak, E., Ivanac, G., Nikiforaki, K., Klontzas, M. E., García-Dosdá, R., Gulsun-Akpinar, M., Lafcı, O., Mann, R., Martín-Isla, C., Prior, F., Marias, K., Starmans, M. P. A., Strand, F., Díaz, O., Igual, L., and Lekadir, K. (2025). A large-scale multicenter breast cancer dce-mri benchmark dataset with expert segmentations. Scientific Data, 12(1):453.
Instituto Nacional de Câncer (INCA), B. (2026). Estimativa 2026: incidência de câncer no Brasil. INCA.
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., and Maier-Hein, K. H. (2021). nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2):203–211.
Jia, J., Staring, M., and Stoel, B. C. (2024). Seg-metrics: A python package to compute segmentation metrics. arXiv preprint arXiv:2403.07884.
Jiang, H. and Nachum, O. (2020). Identifying and Correcting Label Bias in Machine Learning. In Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, pages 702–712. PMLR.
Kleinberg, J., Mullainathan, S., and Raghavan, M. (2017). Inherent trade-offs in the fair determination of risk scores. In Papadimitriou, C. H., editor, 8th Innovations in Theoretical Computer Science Conference (ITCS 2017), volume 67 of Leibniz International Proceedings in Informatics (LIPIcs), pages 43:1–43:23.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
Luczynska, E., Piegza, T., Szpor, J., Heinze, S., Popiela, T., Kargol, J., and Rudnicki, W. (2022). Contrast-Enhanced Mammography (CEM) Capability to Distinguish Molecular Breast Cancer Subtypes. Biomedicines, 10(10):2384.
Makdissi, F. B., Leite, F. P. M., Peres, S. V., e Silva, D. R. M., de Oliveira, M. M., Lopez, R. V. M., Sanches, S. M., Gondim, G. R. M., Iyeyasu, H., Calsavara, V. F., and Curado, M. P. (2019). BREAST CANCER SURVIVAL IN A BRAZILIAN CANCER CENTER: A COHORT STUDY OF 5,095 PATIENTS. Mastology, 29(1):37–46.
Maybury, M. (1990). The mind matters: Artificial intelligence and its societal implications. IEEE Technology and Society Magazine, 9(2):7–15.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6):115:1–115:35.
Mehta, R., Shui, C., and Arbel, T. (2024). Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis. In Medical Imaging with Deep Learning, pages 1453–1492. PMLR.
Oh, Y., Jin, P., Park, S., Kim, S., Yoon, S., Kim, K., Kim, J. S., Li, X., and Li, Q. (2025). Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective. [link].
Parikh, A., Frank, S., Das, S., and Feragen, A. (2026). Towards fairness under label bias in image segmentation: Impact, measurement and mitigation. arXiv preprint arXiv:2605.06891.
Pessach, D. and Shmueli, E. (2020). Algorithmic fairness. arXiv preprint arXiv:2001.09784.
Rabonato, R. T. and Berton, L. (2025). A systematic review of fairness in machine learning. AI and Ethics, 5(3):1943–1954.
Ricci Lara, M. A., Echeveste, R., and Ferrante, E. (2022). Addressing fairness in artificial intelligence for medical imaging. Nature Communications, 13(1):4581.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F., editors, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pages 234–241, Cham. Springer International Publishing.
Tian, Y., Shi, M., Luo, Y., Kouhana, A., Elze, T., and Wang, M. (2024). FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling. International Conference on Learning Representations, 2024:44056–44070.
van Montfort, D., Kok, E., Vincken, K., van der Schaaf, M., van der Gijp, A., Ravesloot, C., and Rutgers, D. (2021). Expertise development in volumetric image interpretation of radiology residents: What do longitudinal scroll data reveal? Advances in Health Sciences Education, 26(2):437–466.
Verma, S. and Rubin, J. (2018). Fairness definitions explained. In Proceedings of the International Workshop on Software Fairness, FairWare ’18, pages 1–7, New York, NY, USA. Association for Computing Machinery.
Verma, V. K., Liang, K. J., Mehta, N., Rai, P., and Carin, L. (2021). Efficient Feature Transformations for Discriminative and Generative Continual Learning. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13860–13870.
Barocas, S. and Selbst, A. D. (2016). Big Data’s Disparate Impact. California Law Review, 104(3):671–732.
Bitencourt, A. G. V. and Chojniak, R. (2021). Indications for breast magnetic resonance imaging in Brazil: Past, present, and future. Radiologia Brasileira, 54:206–206.
Buolamwini, J. and Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency, pages 77–91. PMLR.
Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., and Mahmood, F. (2023). Algorithmic fairness in artificial intelligence for medicine and healthcare. Nature Biomedical Engineering, 7(6):719–742.
Costa, L. D. L. N., Sardinha, A. H. d. L., Verzaro, P. M., Lisbôa, L. L. C., and Batista, R. F. L. (2019). Mortalidade por Câncer de Mama e Condições de Desenvolvimento Humano no Brasil. Revista Brasileira de Cancerologia, 65(1):e–12050.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, ITCS ’12, pages 214–226. Association for Computing Machinery.
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., and Socher, R. (2021). Deep learning-enabled medical computer vision. npj Digital Medicine, 4(1):1–9.
Faiyazuddin, Md., Rahman, S. J. Q., Anand, G., Siddiqui, R. K., Mehta, R., Khatib, M. N., Gaidhane, S., Zahiruddin, Q. S., Hussain, A., and Sah, R. (2025). The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency. Health Science Reports, 8(1):e70312.
Garrucho, L., Kushibar, K., Reidel, C.-A., Joshi, S., Osuala, R., Tsirikoglou, A., Bobowicz, M., Riego, J. d., Catanese, A., Gwoździewicz, K., Cosaka, M.-L., Abo-Elhoda, P. M., Tantawy, S. W., Sakrana, S. S., Shawky-Abdelfatah, N. O., Salem, A. M. A., Kozana, A., Divjak, E., Ivanac, G., Nikiforaki, K., Klontzas, M. E., García-Dosdá, R., Gulsun-Akpinar, M., Lafcı, O., Mann, R., Martín-Isla, C., Prior, F., Marias, K., Starmans, M. P. A., Strand, F., Díaz, O., Igual, L., and Lekadir, K. (2025). A large-scale multicenter breast cancer dce-mri benchmark dataset with expert segmentations. Scientific Data, 12(1):453.
Instituto Nacional de Câncer (INCA), B. (2026). Estimativa 2026: incidência de câncer no Brasil. INCA.
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., and Maier-Hein, K. H. (2021). nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2):203–211.
Jia, J., Staring, M., and Stoel, B. C. (2024). Seg-metrics: A python package to compute segmentation metrics. arXiv preprint arXiv:2403.07884.
Jiang, H. and Nachum, O. (2020). Identifying and Correcting Label Bias in Machine Learning. In Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, pages 702–712. PMLR.
Kleinberg, J., Mullainathan, S., and Raghavan, M. (2017). Inherent trade-offs in the fair determination of risk scores. In Papadimitriou, C. H., editor, 8th Innovations in Theoretical Computer Science Conference (ITCS 2017), volume 67 of Leibniz International Proceedings in Informatics (LIPIcs), pages 43:1–43:23.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
Luczynska, E., Piegza, T., Szpor, J., Heinze, S., Popiela, T., Kargol, J., and Rudnicki, W. (2022). Contrast-Enhanced Mammography (CEM) Capability to Distinguish Molecular Breast Cancer Subtypes. Biomedicines, 10(10):2384.
Makdissi, F. B., Leite, F. P. M., Peres, S. V., e Silva, D. R. M., de Oliveira, M. M., Lopez, R. V. M., Sanches, S. M., Gondim, G. R. M., Iyeyasu, H., Calsavara, V. F., and Curado, M. P. (2019). BREAST CANCER SURVIVAL IN A BRAZILIAN CANCER CENTER: A COHORT STUDY OF 5,095 PATIENTS. Mastology, 29(1):37–46.
Maybury, M. (1990). The mind matters: Artificial intelligence and its societal implications. IEEE Technology and Society Magazine, 9(2):7–15.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6):115:1–115:35.
Mehta, R., Shui, C., and Arbel, T. (2024). Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis. In Medical Imaging with Deep Learning, pages 1453–1492. PMLR.
Oh, Y., Jin, P., Park, S., Kim, S., Yoon, S., Kim, K., Kim, J. S., Li, X., and Li, Q. (2025). Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective. [link].
Parikh, A., Frank, S., Das, S., and Feragen, A. (2026). Towards fairness under label bias in image segmentation: Impact, measurement and mitigation. arXiv preprint arXiv:2605.06891.
Pessach, D. and Shmueli, E. (2020). Algorithmic fairness. arXiv preprint arXiv:2001.09784.
Rabonato, R. T. and Berton, L. (2025). A systematic review of fairness in machine learning. AI and Ethics, 5(3):1943–1954.
Ricci Lara, M. A., Echeveste, R., and Ferrante, E. (2022). Addressing fairness in artificial intelligence for medical imaging. Nature Communications, 13(1):4581.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F., editors, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pages 234–241, Cham. Springer International Publishing.
Tian, Y., Shi, M., Luo, Y., Kouhana, A., Elze, T., and Wang, M. (2024). FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling. International Conference on Learning Representations, 2024:44056–44070.
van Montfort, D., Kok, E., Vincken, K., van der Schaaf, M., van der Gijp, A., Ravesloot, C., and Rutgers, D. (2021). Expertise development in volumetric image interpretation of radiology residents: What do longitudinal scroll data reveal? Advances in Health Sciences Education, 26(2):437–466.
Verma, S. and Rubin, J. (2018). Fairness definitions explained. In Proceedings of the International Workshop on Software Fairness, FairWare ’18, pages 1–7, New York, NY, USA. Association for Computing Machinery.
Verma, V. K., Liang, K. J., Mehta, N., Rai, P., and Carin, L. (2021). Efficient Feature Transformations for Discriminative and Generative Continual Learning. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13860–13870.
Publicado
19/10/2026
Como Citar
HOLANDA, Edson; BERTONL, Lilian.
Evaluating Subgroup Fairness in Deep Learning Models for Breast Cancer Tumor Segmentation. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 23. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1388-1399.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2026.31094.
