From Voxels to Insights: Exploring the Effectiveness and Transparency of Graph Neural Networks in Brain Tumor Segmentation

Accurate brain tumor segmentation is crucial for precise medical diagnosis and treatment planning in medical imaging. This research delves into assessing the effectiveness and transparency of Graph Neural Networks (GNNs) in brain tumor segmentation. The primary objectives include comparing various GNN architectures and improving their understandability by applying the GNNExplainer method. Leveraging the BraTS 2021 challenge dataset, which consists of MRI scans and corresponding ground truth annotations, the study reveals the successful application of GNNs in achieving precise brain tumor segmentation. By incorporating GNNExplainer, the explainability of the models is significantly enhanced, shedding light on the decision-making processes within the network. The proposed approach could advance the field of brain tumor segmentation, providing clinicians with accurate and transparent models to inform their decision-making processes in patient care.
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