Keywords: Kidney, Machine Learning/Artificial Intelligence, Bias Field CorrectionA deep learning model was developed for the differentiation of benign and malignant solid renal masses. A representative and balanced dataset was used, and model performance was evaluated with and without bias field correction (BFC). The model input contained multiple channels for various MRI-based tissue contrast weightings. The model achieved an accuracy and AUC of 74% and 71% with BFC and 80% and 59% without BFC. This work showed that a convolutional neural network can be trained to differentiate benign from malignant renal masses with a high degree of accuracy and that BFC improves AUC.
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