Federated Learning for Brain Tumor Segmentation Using MRI and Transformers

This work focuses on training a deep learning network in a federated learning framework. The Federated Tumor Segmentation Challenge has 2 separate tasks. Task-1 was to design an aggregation logic for a given network, which is trained in a federated learning framework. Task-2 of the challenge was to train a network that is robust and generalizable in a federated testing environment. 341 subjects were used for training both tasks of the challenge. This data was distributed across 17 collaborators, which were then used to train an individual network for each collaborator. A new weight aggregation logic was developed. The network weights in this logic were determined based on the average validation dice scores of each collaborator. A concise model was obtained using the developed weighted aggregation logic. The Dice scores for task-1 on the validation dataset for whole tumor, tumor core, and enhancing tumor were 0.767, 0.612, and 0.628 respectively. The Dice scores for task-2 on the validation dataset for whole tumor, tumor core, and enhancing tumor were 0.874, 0.773, and 0.721 respectively.