Enhancing Breast Cancer Detection and Prognosis through AI/ML-Based Algorithms
Keywords:
Breast cancer, AI/ML-based algorithms, Accuracy, Efficiency, Predictive modeling, HealthcareAbstract
Breast cancer, particularly when it has spread to other regions of the body, presents substantial treatment and prognosis concerns. Researchers have been in the forefront of developing Artificial Intelligence/Machine Learning (AI/ML)-based systems to solve these difficulties. When compared to traditional approaches, these new algorithms provide a viable route for identifying breast cancer with more accuracy and efficiency. In this study, we look at the creation and evaluation of AI/ML-based algorithms for improving breast cancer detection and prognosis. We investigate how these algorithms use cutting-edge technology to increase breast cancer diagnostic accuracy, especially in complicated and advanced stages of the illness. Additionally, we investigate how these algorithms contribute to a better understanding of the prognosis for breast cancer patients, enabling more tailored treatment plans. Our study demonstrates the potential of AI/ML-driven solutions to revolutionize breast cancer detection and prognosis. Through the incorporation of large datasets, advanced image analysis techniques, and predictive modeling, these algorithms offer a significant advancement in the field of oncology. We present evidence of their efficacy, highlighting the crucial role they play in early diagnosis, more accurate prognosis, and ultimately, improved patient outcomes. This research serves as a valuable contribution to the ongoing efforts to combat breast cancer and underscores the transformative potential of AI/ML-based algorithms in the realm of healthcare and disease management.
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References
Cowin, P., Rowlands, T. M., & Hatsell, S. J. (2005). Cadherins and catenins in breast cancer. Current opinion in cell biology, 17(5), 499-508.
Shah, S. M., Khan, R. A., Arif, S., & Sajid, U. (2022). Artificial intelligence for breast cancer analysis: Trends & directions. Computers in Biology and Medicine, 142, 105221.
Ogier du Terrail, J., Leopold, A., Joly, C., Béguier, C., Andreux, M., Maussion, C., ... & Heudel, P. E. (2023). Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer. Nature medicine, 29(1), 135-146.
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor, “Our data, ourselves: Privacy via distributed noise generation,” in Annual International Conference on the Theory and Applications of Cryptographic Techniques. Springer, 2006, pp. 486–503.
C. Dwork, A. Roth et al., “The algorithmic foundations of differential privacy.” Foundations and Trends in Theoretical Computer Science, vol. 9, no. 3-4, pp. 211–407, 2014.
L. Zhao, L. Ni, S. Hu, Y. Chen, P. Zhou, F. Xiao, and L. Wu, “Inprivate digging: Enabling tree-based distributed data mining with differential privacy,” in IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE, 2018, pp. 2087–2095.
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy-preserving machine learning,” in proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 2017, pp. 1175–1191.
P. Mohassel and P. Rindal, “Aby3: A mixed protocol framework for machine learning,” in Proceedings of the 2018 ACM SIGSAC Conference Computer and Communications Security, 2018, pp. 35–52.
S. Hardy, W. Henecka, H. Ivey-Law, R. Nock, G. Patrini, G. Smith, and B. Thorne, “Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption,” arXiv preprint arXiv:1711.10677, 2017.
L. Huang, A. L. Shea, H. Qian, A. Masurkar, H. Deng, and D. Liu, “Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records,” Journal of biomedical informatics, vol. 99, p. 103291, 2019.
N. Dong and I. Voiculescu, “Federated contrastive learning for decentralized unlabeled medical images,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, M. de Bruijne, P. C. Cattin, S. Cotin, N. Padoy, S. Speidel, Y. Zheng, and C. Essert, Eds. Cham: Springer International Publishing, 2021, pp. 378–387.
N. K. Dinsdale, M. Jenkinson, and A. I. Namburete, “Fedharmony: Unlearning scanner bias with distributed data,” arXiv preprint arXiv:2205.15970, 2022.
M. J. Sheller, G. A. Reina, B. Edwards, J. Martin, and S. Bakas, “Multiinstitutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation,” in International MICCAI Brainlesion Workshop. Springer, 2018, pp. 92–104.
W. Li, F. Milletarì, D. Xu, N. Rieke, J. Hancox, W. Zhu, M. Baust, Y. Cheng, S. Ourselin, M. J. Cardoso et al., “Privacy-preserving federated brain tumour segmentation,” in International Workshop on Machine Learning in Medical Imaging. Springer, 2019, pp. 133–141.
C. I. Bercea, B. Wiestler, D. Rueckert, and S. Albarqouni, “Feddis: Disentangled federated learning for unsupervised brain pathology segmentation,” arXiv preprint arXiv:2103.03705, 2021.
Y. Yeganeh, A. Farshad, N. Navab, and S. Albarqouni, “Inverse distance aggregation for federated learning with non-iid data,” in Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning. Springer, 2020, pp. 150–159.
J. Luo and S. Wu, “Fedsld: Federated learning with shared label distribution for medical image classification,” in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022, pp. 1–5
H. R. Roth, K. Chang, P. Singh, N. Neumark, W. Li, V. Gupta, S. Gupta, L. Qu, A. Ihsani, B. C. Bizzo et al., “Federated learning for breast density classification: A real-world implementation,” in Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning. Springer, 2020, pp. 181–191
I. C. Moreira, I. Amaral, I. Domingues, A. Cardoso, M. J. Cardoso, and J. S. Cardoso, “INbreast,” Academic Radiology, vol. 19, no. 2, pp. 236–248, Feb. 2012. [Online]. Available: https://doi.org/10.1016/j.acra.2011.09.014
L. Shen, L. R. Margolies, J. H. Rothstein, E. Fluder, R. McBride, and W. Sieh, “Deep learning to improve breast cancer detection on screening mammography,” Scientific Reports, vol. 9, no. 1, Aug. 2019. [Online]. Available: https://doi.org/10.1038/s41598-019-48995-4
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