A Novel Interpretable Graph Convolutional Neural Network for Multimodal Brain Tumor Segmentation

Deep convolutional neural networks (CNNs) have revolutionized computer vision, demonstrating remarkable performance in various tasks. However, their end-to-end learning strategy poses challenges to explainability. In this work, we explore the application of explainability techniques in brain tumor segmentation using magnetic resonance imaging (MRI) data. Our adaptive learning class activation map (AL-CAM) employs a unique multiple-pop-out training strategy and contrastive learning to enhance internal outputs, improving interpretability. Additionally, we introduce a novel approach to explainability in graph convolutional neural networks (GCNNs). The usage of traditional CNN interpretability tools such as saliency maps, CAM, and EB are often unable to handle the complexity of graph-structured data. Our work brim this gap by adapting and improving these techniques for graph convolutional neural networks (GCNN). We present two innovative tools: adaptive CAM for differentiated interpretability and contrastive EB for deeper insights into functions. Using a novel feature fusion approach, we further push the boundaries and combine the feature strengths of GNN and CNN for a holistic understanding of GCNN decision-making. Our proposed framework enables interpretability in various areas, not just medical imaging. Our work demonstrates the versatility of explainability methods and demonstrates their power in unlocking the secrets of GCNNs and ultimately solving real-world challenges, particularly in the field of medical image analysis.