A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning

Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on the prognosis of the disease and vital evidence for clinical treatment. However, pathological diagnosis is subjective, and differences in observation and diagnosis between pathologists are common. This phenomenon is more evident in hospitals with insufficient medical resources. Deep learning (DL) can be used to identify and classify structures in digital pathology. In order to solve the above difficulties, in this work, we propose a DL framework for generating pathological diagnosis by analyzing histopathological images of renal cell carcinoma. A deep neural network is trained on a large high-quality annotated dataset for accurate tumor area detection, subtyping, and grading. The results show that our framework has achieved pathologist-level accuracy in diagnosis, can generate pathology reports with tumor indicators, and provide pathologists with interpretable auxiliary diagnoses