Federated Learning for Brain Tumor Segmentation in MRI Scans: A Privacy-Preserving and Domain-Adaptable Approach - R, Partheepan; Sivaraju, S.S.; Senthilkumar, A.; Radhika, V.; Rani, N.Suba; Sivarajan, S. (Journal of Neonatal Surgery, 2025)
The application of deep learning in intricate tasks like brain tumour segmentation heavily relies on potent algorithms, which in turn depend on large and heterogeneous datasets that are often fraught with privacy issues or deep domain variations (domain shift). Contra-violent methods concerning MRI data hold little promise as sharing sensitive data captured from patients across various institutions breaches privacy laws like HIPAA; furthermore, variation in scanner equipment and its associated detailing parameters lead to a pathological model building which does not generalise well on new data. This document posits a robust approach to tackle these issues using Federated Learning (FL). With FL, it’s possible to train a model in collaboration with various institutions without sharing raw data from patients. In contrast, model training takes place privately first at each institution, and only learned model updates (i.e., the parameters or gradients of the model) are sent for aggregation on a central server. This allows for the refinement of a global model while keeping patient data safe within their institution. In addition, FL provides and preserves anywhere the variance of medical imaging data within one defined place as compared to traditional approaches, boosting the model resilience and flexibility to cope with new challenges due to changing MRI protocol shifts. This approach included first locally training a Convolutional Neural Network (CNN) on data partitioned at each client site and then aggregating the model weights at a central location. The model was evaluated at the client based on the loss incurred during training and accuracy during prediction, achieving the intended goals. Despite experiencing certain issues such as data heterogeneity (as shown by one client's loss oscillation), the model was able to learn from the distributed data and predict tumours correctly on the MRI scans. This study concludes that federated learning provides an effective framework with privacy-preserving characteristics and flexible domain adaptation for constructing resilient brain tumour segmentation models with multiple decentralised MRI datasets.