TCA-Net: A Tri-Stage Cross-Attention Network with Explainability-Consistency and Boundary-Aware Learning for Generalizable Medical Image Segmentation

Existing medical image segmentation methods often address cross-site generalization, boundary accuracy, and explainability independently, leaving a gap in their unified integration. To address this, we propose TCA-Net, a novel CNN–Transformer framework that combines four-level Tri-Stage Cross-Attention fusion with training-time XAI-Consistency learning and boundary-aware supervision. Unlike conventional feature-fusion methods, TCA-Net explicitly captures complementary channel, spatial, and cross-attention information while promoting anatomically meaningful and precise segmentation. The framework is validated using leakage-free cross-site evaluation and controlled ablation studies, achieving 96.28% Dice in-domain and up to 98.29% Dice across sites.