CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma

Abstract

A transformer-based network performs better than traditional deep learning algorithms in terms of the accuracy of ccRCC prediction. Meanwhile, the transformer has a certain advantage in dealing with noise labels existing in CT images of ccRCC. This method is promising to be applied to other medical tasks (e.g., the grade of neurogliomas and meningiomas).