Swarm intelligence-guided ROI selection for deep learning assessment of HER2 in colorectal cancer

Accurate assessment of Human Epidermal Growth Factor Receptor 2 (HER2) status in colorectal cancer (CRC) is pivotal for precision therapy, yet the gigapixel resolution of Whole Slide Images (WSIs) presents a significant computational bottleneck for traditional deep learning workflows that rely on exhaustive sliding-window tiling. Addressing this challenge, we propose a novel coarse-to-fine framework that mimics the pathologist's cognitive screening process by integrating swarm intelligence with deep learning. Specifically, we treat the low-magnification WSI as a two-dimensional search space and employ a Particle Swarm Optimization (PSO) algorithm to autonomously navigate and identify diagnostically relevant Regions of Interest (ROIs). The PSO search is guided by a fitness function based on color deconvolution metrics-prioritizing the proportion and intensity of 3,3'-Diaminobenzidine (DAB) staining-thereby effectively filtering out non-informative background and negative tissue without the need for full-slide scanning. In the subsequent stage, these high-value candidate ROIs are extracted at high resolution and analyzed using state-of-the-art deep learning models, such as ResNet or Swin Transformer, to classify HER2 status. Experimental results demonstrate that this swarm intelligence-driven approach reduces computational overhead while achieving favorable patch-level discrimination by focusing analysis on key pathological areas. To clarify how the selected ROI patches behave at the WSI level, we further report per-WSI prediction-count distributions and include an Attention-Based Multiple Instance Learning (ABMIL) baseline. By combining intelligent sampling with deep learning classification, our method provides an interpretable and computationally efficient ROI preselection framework for digital pathology workflows, while broader multi-center validation will be required before clinical deployment.