APA (7th ed.) Citation

Liang, Q., Adkinson, B. D., Jiang, R., Scheinost, D., Goos, G., Hartmanis, J., . . . Schnabel, J. A. (2024). Overcoming Atlas Heterogeneity in Federated Learning for Cross-Site Connectome-Based Predictive Modeling. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 : 27th International Conference, Marrakesh, Morocco, October 6–10, 2024, Proceedings, Part X, 15010, 579-588. https://doi.org/10.1007/978-3-031-72117-5_54

Chicago Style (17th ed.) Citation

Liang, Qinghao, et al. "Overcoming Atlas Heterogeneity in Federated Learning for Cross-Site Connectome-Based Predictive Modeling." Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 : 27th International Conference, Marrakesh, Morocco, October 6–10, 2024, Proceedings, Part X 15010 (2024): 579-588. https://doi.org/10.1007/978-3-031-72117-5_54.

MLA (9th ed.) Citation

Liang, Qinghao, et al. "Overcoming Atlas Heterogeneity in Federated Learning for Cross-Site Connectome-Based Predictive Modeling." Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 : 27th International Conference, Marrakesh, Morocco, October 6–10, 2024, Proceedings, Part X, vol. 15010, 2024, pp. 579-588, https://doi.org/10.1007/978-3-031-72117-5_54.