Meta-Adaptive Neuro-Symbolic Causal Graph Transformer for Explainable Brain Tumor Segmentation and Progression Analysis
Brain tumor segmentation from MRI remains a critical yet complex challenge in neuro-oncology due to tumor heterogeneity, ambiguous boundaries, and variability across imaging institutions. Although state-of-the-art deep learning techniques have been successful in achieving high levels of precision in brain tumor segmentation, there still are several limitations in terms of transparency, causal reasoning, and robustness of such systems which restrict their applicability in clinical decision support systems. Further, the majority of explainable AI strategies offer post-hoc visualizations, but do not offer either causal or temporal logic, limiting clinical confidence. In order to overcome these constraints, a Meta-Adaptive Neuro-Symbolic Causal Graph Transformer (MANS-GT) is suggested. The model combines the elements of deep neural perception, causal inference, and symbolic reasoning to provide the results of both proper interpretation and explainable progression analysis. The proposed framework employs a Hybrid CNN-Vision Transformer encoder for multimodal MRI feature extraction, a Multi-Scale Attention U-Net (MAU-Net) for tumor-region delineation, and a Graph Attention-based Structural Casual Model (GA-SCM) for modelling relationships among morphological and clinical variables. A Causal Neuro-Symbolic Reasoner (CNSR) is a neural embedding translator to generate chains of logical rules, which are computable by human experts and whose diagnostic reasoning can be interpreted by clinicians. A meta-adaptive learning layer that is a combination of Model-Agnostic Meta-Learning (MAML) and Domain Adaptation Normalization (DAN) is used to guarantee the generalization of cross-domain across institutions and imaging modalities. MANS-GT, implemented in Python 3.10 with PyTorch 2.2 and NVIDIA RTX 4090, has better Dice scores (ET: 90.3, TC: 92.1, WT: 90.4) and cross-dataset consistency. It is not only better at segmentation precision and causal interpretability but also offers clinically traceable reasoning of tumor progression to create a scalable and explainable AI paradigm of real-world neuro-oncological imaging.