Bridging Global Attention and Local Hierarchies: A Robust Hybrid Ensemble Framework With Multi-Perspective Explainability for Automated HER2-IHC Scoring.

IntroductionDetermining the HER2 status accurately is a critical determinant in breast cancer treatment planning. Manual scoring remains highly susceptible to inter- and intra-observer variability, particularly in diagnostically ambiguous cases (HER2 1+/2+). Furthermore, contemporary deep learning models frequently lack the robustness and interpretability required for safe clinical integration.MethodsWe propose a hybrid ensemble framework that synergizes EVA-02-Large, Vision Transformer-Base, and ConvNeXt-V2-Nano architectures via an adaptive late-fusion mechanism. The model was trained on the expert-annotated HER2-IHC-40x dataset (N=10,997 patches) and rigorously evaluated across ten independent trials. The experimental protocol incorporated comprehensive perturbation analysis across 20+ image corruption types and multi-perspective Explainable AI (XAI) assessment using Grad-CAM, Grad-CAM++, and Layer-CAM to validate alignment with pathologist-recognized morphological features.ResultsThe fused ensemble achieved state-of-the-art weighted accuracy (98.05% ± 0.10%) and Balanced Accuracy (98.25% ± 0.11%), outperforming the strongest single backbone by 0.45%. Misclassification rates for equivocal classes decreased by >18%. The framework demonstrated high resilience to structural perturbations (mCE < 0.05) while exhibiting expected sensitivity to extreme photometric variations. XAI analysis confirmed that model attention consistently prioritizes clinically relevant membrane staining patterns, mirroring expert diagnostic reasoning.ConclusionThis study establishes a highly accurate and interpretable pipeline for automated HER2-IHC scoring, demonstrating that hybrid transformer-CNN ensembles can effectively resolve diagnostically challenging cases. By combining superior predictive performance, structural robustness, and transparent decision-making, the proposed framework offers a highly promising foundational framework that, following prospective multi-reader clinical validation, could serve as a robust decision-support tool to standardize HER2 assessment, reduce inter-observer variability, and accelerate precision oncology workflows.