MPGR-Net: multi-scale patch graph reasoning for weakly supervised pathological image segmentation

Weakly supervised pathological image segmentation aims to localize disease regions using coarse annotations (e.g., image-level labels), which significantly reduces annotation cost but introduces challenges such as imprecise localization and noisy predictions. In this paper, we propose multi-scale patch graph reasoning network (MPGR-Net), a novel framework that integrates multi-scale graph reasoning into transformer-based architectures to address these limitations. Specifically, we first employ a Vision Transformer encoder to extract global-aware patch representations. To explicitly model structured spatial dependencies that are often overlooked by standard self-attention, we introduce a multi-scale patch graph reasoning module, where patch tokens are treated as graph nodes and connected via predefined spatial adjacency at multiple scales. This design enables effective aggregation of local and long-range contextual information while maintaining computational efficiency. Furthermore, we incorporate a lightweight cross-attention mechanism to enhance global-to-local feature interaction through channel-wise modulation. Extensive experiments on pathological image segmentation benchmarks demonstrate that MPGR-Net consistently outperforms existing weakly supervised methods, achieving superior segmentation accuracy and robustness. Ablation studies further validate the effectiveness of the proposed graph reasoning module.