Deep learning approach for renal cell carcinoma detection, subtyping, and grading

We propose a comprehensive end-to-end pipeline designed for the detection, subtyping, and grading of tumors. Our proposed method- ology initiates the generation of a heat map, indicating the severity of the tumor. Subsequently, the identification of the most critical patches is conducted based on the probability scores. These iden- tified patches are then directed to a grade prediction network. A distinctive aspect of our research lies in being the first to explore an end-to-end pipeline for both heat map generation and grading pre- diction. Our experiments were conducted leveraging the public, The Cancer Genome Atlas (TCGA) repository, focusing specifically on renal cancer. We introduced additional patch-level labels to improve the model performance. The generation of tumor heat maps targeted three primary cancer subtypes: clear cell, papillary, and chromo- phobe. To enhance our approach, we implemented center-loss and introduced a method aimed at refining the quality of patches. The experimental outcomes highlight superior performance compared to state-of-the-art method. This research contributes to the advance- ment of tumor detection and grading, emphasizing the significance of an integrated approach for heat map generation and grading pre- diction. ### Competing Interest Statement The authors have declared no competing interest.