We wish to acknowledge that scientific work exceeds the limits of individual projects.

The publications are listed here based on the co-authorship of one or more PRIVASA researchers (in bold). These represent research activities
planned and executed in the project or collaborative research activities
within the thematic scope of the project.

Eisenmann M., Reinke A., Weru V., Tizabi M. D., Isensee F., Adler, T. J., … Jafaritadi M., Kontio E., Khan M.,  … & Finzel R. (2022). Biomedical image analysis competitions: The state of current participation practice. arXiv preprint

Huhtanen J.T., Nyman M., Doncenco D., Hamedian M., Kawalya D., Salminen L., Sequeiros R.B., Koskinen S.K., Pudas T.K., Kajander S., Niemi P., Hirvonen J., Aronen H., Jafaritadi M. (2022). Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs. Scientific Reports 12, 11803. https://doi.org/10.1038/s41598-022-16154-x

Kaisti M., Laitala J., Wong D., Airola A. (2023). Domain randomization using synthetic electrocardiograms for training neural networks. Artificial Intelligence in Medicine. https://doi.org/10.1016/j.artmed.2023.102583

Khan M.I., Azeem M. A.,  Alhoniemi E., Kontio E., Khan S.A., Jafaritadi M. (2023). Regularized weight aggregation in networked federated learning for glioblastoma segmentation.  arXiv preprint

Khan M. I., Alhoniemi E., Kontio E., Khan S. A., Jafaritadi M. (2023) Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation. arXiv prerint

Khan M.I., Alhoniemi E., Kontio E., Khan S.A., Jafaritadi M. (2023). RegAgg: A Scalable Approach for Efficient Weight Aggregation in Federated Lesion Segmentation of Brain MRIs. 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC). https://doi.org/10.1109/FMEC59375.2023.10306171

Khan M.I., Jafaritadi M., Alhoniemi E., Kontio E., Khan S.A. (2022). Adaptive Weight Aggregation in Federated Learning for Brain Tumor Segmentation. In: Crimi A., Bakas S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_40

Marelli L., Stevens M., Sharon T., Van Hoyweghen I., Boeckhout M., Colussi I., Degelsegger-Márquez A., El-Sayed S., Hoeyer K., van Kessel R.,Krekora Zając D., Matei M., Roda S., Prainsack B., Schlünder I., Shabani M., Southerington T. (2023). The European Health Data Space: Too Big To Succeed?. Health Policy, 104861. https://doi.org/10.1016/j.healthpol.2023.104861

Montoya Perez I., Movahedi P., Nieminen V., Airola A., Pahikkala T. (2024) Does Differentially Private Synthetic Data Lead to Synthetic Discoveries? Methods of Information in Medicine. https://doi.org/10.1055/a-2385-1355

Movahedi P., Nieminen V., Perez I. M., Pahikkala T. and Airola A. (2023) Evaluating Classifiers Trained on Differentially Private Synthetic Health Data. IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). https:/doi.org/10.1109/CBMS58004.2023.00313

Movahedi P., Nieminen V., Montoya Perez I., Daafane H., Sukhwal D., Pahikkala T., Airola A. (2024) Benchmarking Evaluation Protocols for Classifiers Trained on Differentially Private Synthetic Data. IEEE Access 12: 118637-118648. https://doi.org/10.1109/ACCESS.2024.3446913

Nieminen V., Pahikkala T., and Airola A. (2023) Empirical evaluation of amplifying privacy by subsampling for GANs to create differentially private synthetic tabular data. CEUR Workshop Proceedings. Link to publication.

Salmi J., Hermansson L.-L. (2022) Centralized or de-centralized data and algorithms in the Finnish health care infrastructure. 14th International Conference on eHealth.

Vaiste J. (2023) Ethical implications of AI-generated synthetic health data. 2023. HAL preprint ⟨hal-04216538⟩