Microsatellite instability （MSI） is a key biomarker for cancer prognosis and treatment decisions，particularly in colorectal and gastric cancers. With the widespread application of digital pathology，whole slide image （WSI） have become an important data source for MSI prediction. However，automatically predicting MSI from WSIs remains challenging due to their gigapixel scale，insufficient supervised information at the slice level，and considerable heterogeneity in histopathological tissue structures. Most existing methods rely on multiple instance learning strategies，processing WSIs by segmenting them into isolated image patches and aggregating block-level predictions using pooling strategies. While this approach is effective to some extent，it inherently ignores the spatial and semantic dependencies between tissue regions，leading to poor representation learning performance. Furthermore，pathological diagnosis is inherently a multiscale process，requiring joint reasoning about global tissue structure and local cell morphology. Many current methods either operate at only a single magnification or employ coarse and nonadaptive multiscale fusion strategies，limiting their ability to capture complementary information across scales. Therefore，this work aims to develop a weakly supervised WSI modeling framework that explicitly captures structural relationships between patches while effectively integrating complementary information across multiple magnification scales to achieve reliable MSI classification.

To achieve this goal，this research proposes a graph edge-attribute attentional multiscale fusion network （GEAMF）. In GEAMF，each WSI is represented as a graph，where image patches correspond to nodes，and interpatch relationships are modeled as edges. First，this study designs a graph construct module（GC-M） that combines constraints on semantic similarity and spatial proximity to establish reliable structural connections between image patches. Specifically，cosine similarity can effectively measure semantic alignment in a high-dimensional feature space，but it can introduce nonphysical long-range edges in heterogeneous WSI. These connections propagate irrelevant contextual information to distant tissue regions. To address this limitation，we introduce Euclidean distance as a spatial constraint to reweight semantic similarity，thereby achieving locally perceptive edge modeling. This design explicitly balances semantic consistency and spatial coherence，achieving graph modeling that is locally perceptive and structurally meaningful. On the basis of this graph representation，this study proposes a graph edge-attribute attention module （GEA-M） to perform structure-aware message passing. In contrast to traditional graph attention mechanisms that only utilize node features，GEA-M explicitly incorporates edge attributes into the attention computation. This design allows the model to adjust the information propagation intensity dynamically on the basis of the discriminative importance of different structural relationships，suppressing noisy or irrelevant connections while emphasizing diagnostically remarkable interactions between tissue regions. To address the inherent multiscale nature of pathological diagnosis further，GEAMF employs a dual architecture，independently processing WSI at two magnification levels. Each branch learns a graph-level representation for its specific scale，capturing either global organizational structure or fine-grained cell morphology. To integrate these heterogeneous representations adaptively，this work further designs a scale-aware gated ResMLP module （SGRes-M）. This module introduces learnable scale-aware gating，dynamically reweighting the magnification contribution of each sample，and then uses a residual multilayer perceptron to model higher-order interactions between scales while preserving scale-specific discriminative cues.

This study systematically evaluates the proposed GEAMF model on several publicly available WSI benchmark datasets. The TCGA_CRC and TCGA_STAD datasets were used to evaluate the model’s performance on the MSI classification task，while the Camelyon16 dataset was used to test the model’s generalization ability in different pathological task scenarios. On the TCGA_CRC dataset，GEAMF achieved Acc，AUC，and F1 of 0.945，0.835，and 0.887. On the TCGA_STAD dataset，the model achieved Acc，AUC，and F1 of 0.924，0.829，and 0.897，respectively. Furthermore，GEAMF also achieved good performance on the Camelyon16 lymph node metastasis detection dataset，with Acc，AUC and F1 of 0.907，0.877 and 0.848，validating broad generalization on different tasks. Compared with various existing WSI classification methods，the proposed model demonstrates more stable and superior overall performance across different datasets and task settings. To validate the rationality of each module design further，this study systematically analyzes the impact of the proposed modules on model performance through ablation experiments. Furthermore，this study improves the model’s interpretability from two levels： t-SNE and attention heatmaps.

This study proposes a novel weakly supervised framework，GEAMF for WSI classification，which explicitly integrates graph-based structural modeling and adaptive multiscale fusion. By introducing an edge-attribute-aware attention mechanism and scale-aware gated residual fusion，GEAMF effectively captures intrascale structural dependencies and cross-scale complementary information. Experimental results on the MSI benchmark datasets demonstrate that explicitly modeling the structural relationships between image patches and combining them with multiscale representations remarkably improves classification performance，validating the effectiveness of introducing structure-aware and scale-aware inductive bias into WSI analysis. Furthermore，GEAMF exhibits stable performance in the Camelyon16 lymph node metastasis detection task，further illustrating the framework’s generalization ability across different pathological tasks. This framework provides a general and scalable paradigm for graph-based multiscale modeling in computational pathology and has potential applications in various histopathological prediction tasks.