Approximate Bilevel Graph Structure Learning for Histopathology Image Classification

The structural and spatial arrangements of cells within tissues represent their functional states, making graph-based learning highly suitable for histopathology image analysis. Existing methods often rely on fixed graphs with predefined edges, limiting their ability to capture the true biological complexity of tissue interactions. In this work, we propose ABiG-Net (Approximate Bilevel Optimization for Graph Structure Learning via Neural Networks), a novel framework designed to learn optimal interactions between patches within whole slide images (WSI) or large regions of interest (ROI) while simultaneously learning discriminative node embeddings for the downstream image classification task. Our approach hierarchically models the tissue architecture at local and global scales. At the local scale, we construct patch-level graphs from cellular orientation within each patch and extract features to quantify local structures. At the global scale, we learn an image-level graph that captures sparse, biologically meaningful connections between patches through a first-order approximate bilevel optimization strategy. The learned global graph is optimized in response to classification performance, capturing the long-range contextual dependencies across the image. By unifying local structural information with global contextual relationships, ABiG-Net enhances interpretability and downstream performance. Experiments on two histopathology datasets demonstrate its effectiveness: on the Extended CRC dataset, ABiG-Net achieves 97.33 $\pm$ 1.15 % accuracy for three-class colorectal cancer grading and 98.33 $\pm$ 0.58 % for binary classification; on the melanoma dataset, it attains 96.27 $\pm$ 0.74 % for tumor-lymphocyte ROI classification.