Multi-Objective Surrogate-Assisted Neural Architecture Search for Digital Pathology: A Tree-Based Regression Approach

Abstract

Digital pathology enables histopathological slide digitization, becoming crucial in modern medicine. While deep learning models excel at classification, their success requires carefully designed architectures, making manual design expensive. Neural Architecture Search (NAS) automates this process, but high evaluation costs limit these methods. Surrogateassisted NAS addresses this by predicting architecture performance without full training. We propose a multiobjective surrogate-assisted NAS method for digital pathology whole slide image classification. Our approach simultaneously minimizes classification error and parameter count using Path-based Multi-Objective Differential Evolution with opposition-based learning. Unlike CNN-based surrogates, we employ tree-based regression models (CatBoost, LightGBM, XGBoost). EBHI dataset experiments show model complexity directly impacts accuracy and inference time. The CatBoost-High model achieved the highest accuracy (98.73%), while XGBoost-Medium provided optimal trade-off (98.46% accuracy, 250s). Results demonstrate our method achieves high accuracy while significantly reducing computational cost, making it promising for digital pathology applications.