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 projected 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 classification. Specifically

we introduce the multilayer spatial feature module to enhance morphological feature representation 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 framework exhibits superior practicality and robustness in RCC pathology grading tasks.