Federated Learning with Privacy Preserving for Multi- Institutional Three-Dimensional Brain Tumor Segmentation
This study presents a highly competitive, collaborative federated learning model with PPTs that can successfully segment brain tumor lesions without compromising patient data confidentiality. Future work will improve model generalizability and extend the framework to other medical imaging tasks.
Authors
- Rawad Abdulghafor (ORCID: https://orcid.org/0000-0002-0266-1027)
- Sherzod Turaev (ORCID: https://orcid.org/0000-0001-6661-8469)
- Akram Bennour (ORCID: https://orcid.org/0000-0002-3185-8806)
- Mohammed Al-Sarem (ORCID: https://orcid.org/0000-0001-7172-8224)
- Mohamed Gasmi (ORCID: https://orcid.org/0000-0001-5872-9142)
- Abdulaziz Aborujilah (ORCID: https://orcid.org/0000-0002-7497-4321)
- Makhlouf Derdour (ORCID: https://orcid.org/0000-0001-6622-4355)
- Mohammed Elbachir Yahiaoui
Institutions
- Arab Open University (OM)
- United Arab Emirates University (AE)
- Larbi Ben M'hidi University of Oum El Bouaghi (DZ)
- Université Larbi Tébessi (DZ)
- Yemenia University (YE)
- Dhofar University (OM)
Publication Details
- Journal
- Diagnostics
- Published
- 2024-12-23
- DOI
- https://doi.org/10.3390/diagnostics14242891
- Citations
- 29
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 10.07
Funders
- United Arab Emirates University