A bias-resilient client selection analysis for federated brain tumor segmentation

Brain tumor segmentation is difficult because of a number of technical problems, including its complex morphology, individual differences in anatomy, irregular shapes, overlapping, homogeneous gray matter and white matter intensity values, abnormalities that might not contrast with normal tissues, and the possibility of additional complications from various modalities. Expert radiologists may make different conclusions as a result of these difficulties. Regarding this, deep learning techniques, particularly CNN models, can be trained to handle these MRI artifacts and automatically extract features that the human eye is unable to detect, such as variations in shape, texture, and color. Deep learning models may effectively learn features across various modalities, but they are data-hungry techniques that could be enhanced with additional annotated data. Yet, data privacy is the main barrier to the real use of data centralization. To deal with these challenges proposed a federated learning approach. The proposed federated learning enables the decentralized learning of a shared model while sharing data. However, the traditional paradigm introduced in the literature involves institutional biases that have an impact on distributed learning. Proposed FedWCEBTD (Federated Learning with Weak Client Elimination for Brain Tumor Detection) is a combination of a modified UNet architecture and federated. In addition, proposed method uses an optimal adaptive client selection strategy by carefully choosing each client based on their unique strengths. Our contribution to this crucial and costly diagnostic is being validated using the BRATS 2021 dataset, taking into account the slicing of brain tumors. The goal of this research is to outperform non-federated learning or perform on par with non-federated environments. At present, the suggested model is outperforming the others by 1% for detection of enhancing tumor and necrosis. The efficiency of the proposed federated learning was demonstrated by considerably higher dice-coefficient of enhancing tumor (p< 0. 05) as compared to non-federated learning. However, the edema identification dice coefficient is 80%, which is similar to the baseline.