  Data augmentation refers to a group of techniques whose goal is to battle
limited amount of available data to improve model generalization and push
sample distribution toward the true distribution. While different augmentation
strategies and their combinations have been investigated for various computer
vision tasks in the context of deep learning, a specific work in the domain of
medical imaging is rare and to the best of our knowledge, there has been no
dedicated work on exploring the effects of various augmentation methods on the
performance of deep learning models in prostate cancer detection. In this work,
we have statically applied five most frequently used augmentation techniques
(random rotation, horizontal flip, vertical flip, random crop, and translation)
to prostate Diffusion-weighted Magnetic Resonance Imaging training dataset of
217 patients separately and evaluated the effect of each method on the accuracy
of prostate cancer detection. The augmentation algorithms were applied
independently to each data channel and a shallow as well as a deep
Convolutional Neural Network (CNN) were trained on the five augmented sets
separately. We used Area Under Receiver Operating Characteristic (ROC) curve
(AUC) to evaluate the performance of the trained CNNs on a separate test set of
95 patients, using a validation set of 102 patients for finetuning. The shallow
network outperformed the deep network with the best 2D slice-based AUC of 0.85
obtained by the rotation method.
