EGA-Ploc: An Efficient Global-Local Attention Model for Multi-Label Protein Subcellular Localization Prediction on the Immunohistochemistry Images

Protein subcellular localization (PSL) is central to unraveling protein functions and disease mechanisms in bioinformatics. Immunohistochemistry (IHC) images serve as rich sources of high-resolution visual cues for PSL prediction. However, conventional deep learning approaches face critical limitations: whole-image models suffer irreversible fine-grained detail loss during downsampling, while patch-based methods lack effective global context integration. Additionally, the long-tailed class distribution in PSL datasets exacerbates performance degradation for underrepresented classes. To address these challenges, we present EGA-Ploc, a framework employing a linear attention mechanism optimized for high-resolution IHC images. This mechanism enables efficient global and local feature modeling with near-linear computational complexity, facilitating end-to-end processing of original images without resolution loss. Moreover, we propose an adaptive multi-label loss function that integrates zero-bounded log-sum-exp constraints with dynamic class-weighted compensation to mitigate dataset imbalance. Consequently, our EGA-Ploc achieves competitive performance across multiple PSL benchmarks while maintaining computational efficiency superior to existing methods. Through extensive visualization analysis, we further investigate the generalizability of off-the-shelf computer vision models in PSL, uncovering interpretable insights into their subcellular localization mechanisms.