  Convolutional Neural Networks (CNNs) have been used for automated detection
of prostate cancer where Area Under Receiver Operating Characteristic (ROC)
curve (AUC) is usually used as the performance metric. Given that AUC is not
differentiable, common practice is to train the CNN using a loss functions
based on other performance metrics such as cross entropy and monitoring AUC to
select the best model. In this work, we propose to fine-tune a trained CNN for
prostate cancer detection using a Genetic Algorithm to achieve a higher AUC.
Our dataset contained 6-channel Diffusion-Weighted MRI slices of prostate. On a
cohort of 2,955 training, 1,417 validation, and 1,334 test slices, we reached
test AUC of 0.773; a 9.3% improvement compared to the base CNN model.
