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Federated Learning with Privacy Preserving for Multi- Institutional Three-Dimensional Brain Tumor Segmentation
Mohammed Elbachir Yahiaoui1, Makhlouf Derdour2, Rawad Abdulghafor3
1Mathematics, Informatics and Systems LAboratory-LAMIS Laboratory, University of Echahid Cheikh Larbi Tebessi, Tebessa 12000, Algeria.
Diagnostics (Basel, Switzerland)
|January 8, 2025
Summary
This study introduces a privacy-preserving federated learning model for accurate brain tumor segmentation using 3D U-Net. The approach effectively segments tumors while protecting patient data confidentiality.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Biology
Background:
- Accurate brain tumor diagnosis is critical, but data sharing for deep learning is hindered by privacy and legal barriers.
- Federated learning (FL) offers a solution for collaborative model training without direct data sharing.
- Privacy-preserving techniques (PPTs) are essential to ensure data confidentiality in FL.
Purpose of the Study:
- To implement a federated learning approach for brain tumor segmentation.
- To integrate privacy-preserving techniques (PPTs) for enhanced data confidentiality.
- To address challenges in medical imaging data sharing for deep learning models.
Main Methods:
- Utilized a 3D U-Net model trained with federated learning on the BraTS 2020 dataset.
- Incorporated differential privacy as a PPT to safeguard patient data.
- Evaluated segmentation performance using Dice similarity coefficients (DSCs) and 95% Hausdorff distances (HD95) for whole tumor (WT), tumor core (TC), and enhancing tumor core (ET).
Main Results:
- The federated model achieved competitive DSCs and HD95 values on validation and test sets.
- On the test set, DSCs reached 89.85% (WT), 87.55% (TC), and 86.6% (ET), with HD95 values of 22.95 mm, 8.68 mm, and 8.32 mm, respectively.
- Demonstrated the effectiveness of the segmentation approach and its privacy preservation capabilities.
Conclusions:
- A collaborative federated learning model with PPTs successfully segments brain tumor lesions without compromising patient confidentiality.
- The developed model shows high performance in brain tumor segmentation.
- Future work will focus on improving model generalizability and expanding the framework to other medical imaging tasks.
