Understanding the Role of Physics-Informed Inductive Biases in Brain Tumor Segmentation: A Theoretical and Methodological Perspective

Physics-informed deep learning models have received increasing attention in medical image segmentation and particularly in brain tumor analysis, owing to their ability to incorporate mechanistic prior knowledge into data-driven architectures. Although numerous studies have presented empirical observations using physics-informed constraints, considerably less attention has been paid to why, when, and under what conditions such prior knowledge meaningfully contributes to segmentation behavior. Thi