FPNuNet

FPNuNet addresses these limitations through a frequency-aware prompt-guided architecture that integrates RGB and Hematoxylin-Eosin-Diaminobenzidine channels via a color fusion stem, processes features through SAM-based structural and ViT-based semantic encoders modulated by lightweight prompt adapters, and employs a progressive frequency-aware residual global fusion neck to aggregate multi-scale features. The network utilizes three collaborative decoder branches to jointly predict binary masks, horizontal-vertical vectors, and nuclear types, enabling robust performance across diverse IHC staining conditions. Experimental evaluation on the CD47-IHCNuSC dataset demonstrates that FPNuNet consistently outperforms state-of-the-art baselines in both segmentation accuracy and classification robustness.