Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging

Colorectal Cancer (CRC) is one of the most common cancer diagnosed in humans. Outcomes vary significantly among patients with different tumor status. Accurate staging of colorectal cancer for personalized treatment is thus highly desired. Whole slide pathological images (WSIs) serves as the gold standard for Tumour Node Metastasis (TNM) staging. However, TNM staging for colorectal cancer relies on labor-intensive manual discriminative patch labeling, which is not suitable and scalable for large-scale WSIs TNM staging. Though various methods have been proposed to select key image patches to perform staging, they are unable to consider the structure of tissue types in biopsy samples which is a key evidence for determining tumor status. In this paper, we propose a Graph Attention Multi-instance Learning (Graph Attention MIL) with texture features, which encodes a spatial structure between patches and jointly predicts the TNM staging. We evaluated our proposed method on a large cohort of colorectal cancer dataset. The proposed framework improves the performance over the existing state-of-the-art methods indicating the future research towards graph based learning for TNM staging.