Automated Diagnosis of Renal Sinus Invasion in Renal Cell Carcinoma with Contrastive Learning and Proxy Task

The accurate assessment of sinus invasion plays a crucial role in determining the surgical approach and prognosis evaluation in renal cell carcinoma. Clinical practice often relies on histopathological examination, which is timeconsuming and invasive for patients. Deep learning is frequently employed to enhance physicians' efficiency and assist clinical diagnosis and treatment from an imaging perspective. However, data imbalance and inter-patient variations pose challenges to neural network feature extraction and generalization. In this study, we propose a proxy task based 3D ResNet network for predicting sinus involvement in renal cell carcinoma to effectively aid physicians in diagnosing and treating sinus invasion. The proposed network leverages proxy tasks and a 3D attention mechanism to extract finer feature representations from CT images, thus improving the accuracy of sinus involvement prediction. We evaluate the performance of the proposed network on multi-center data. The results demonstrate that our approach achieves state-ofthe-art performance compared to other competitive methods.