Existing MIL pipelines for whole-slide image classification often rely on many high-magnification patches, creating redundant inputs while underusing spatial structure. MG-Trans addresses this by combining patch anchoring, dynamic structure learning, and a multi-scale information bottleneck within a graph-transformer framework. The resulting model captures fine-grained morphology more efficiently and strengthens discriminative whole-slide representations.