摘要The pathological classification of renal cell carcinoma(RCC)is a critical indicator of its accurate diagnosis,treatment,and prognosis.Pathologists typically focus on a single subtype when determining classifications,whereas existing multiple instance learning approaches often lack the global modeling of instance-level features and fail to capture contextual dependencies.When multiple instances with large semantic disparities are pro-jected into the same latent space,structural information loss or the dilution of critical pathological features may occur,thereby limiting classification performance.To address these challenges,we propose an end-to-end Transformer-based structured low-rank multiple instance learning framework,termed TSMIL,for RCC classifi-cation.Specifically,we introduce the multilayer spatial feature module to enhance morphological feature re-presentation and the structured low-rank block to embed high-dimensional features into a low-rank structure,effectively capturing contextual information and exploring the latent semantic potential of pathological features in a sparse representation space.Extensive experiments demonstrate that our proposed TSMIL achieves a mean accuracy of 92.98％and an AUC of 0.9818,outperforming other state-of-the-art methods.Overall,our frame-work exhibits superior practicality and robustness in RCC pathology grading tasks.