A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-Weighted MRI Using Convolutional Neural Networks

Abstract

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.

Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review

“…Three out of 12 DL studies (25%) [ 37 – 39 ] that underwent quality screening using CLAIM failed at least three pre-identified mandatory criteria, with 2/12 [ 40 , 41 ] failing two, and 2/12 [ 42 , 43 ] failing just one criterion. Four of the seven rejected papers (57%) [ 37 – 39 , 43 ] did not describe data processing steps in sufficient detail (Q9), 4/7 [ 38 – 40 , 42 ] did not explain the exact method of selecting the final model (Q26), and 3/7 [ 38 , 40 , 41 ] failed to provide enough details on training approach (Q25).…”

Locoregional Recurrence Prediction Using a Deep Neural Network of Radiological and Radiotherapy Images

“…All combinations of flipping directions were applied to produce 8 times larger training data sets. Such data augmentation is a useful strategy to deal with small data set [31]. Three-dimensional DL models were optimized until the minimum validation loss was achieved as shown in Figure S1.…”

A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning

“…Transfer learning is a technique that pretrains a CNN on a large labeled dataset and then fine-tunes the pretrained CNN on the target dataset, which can effectively speed up network convergence without compromising model performance [ 15 ]. Transfer learning has been successfully applied in the field of medical imaging, such as brain tumor classification task [ 16 – 19 ], prostate cancer recognition task [ 20 , 21 ], and diabetic retinopathy grading task [ 22 ]. The active learning algorithm is another effective strategy for minimizing the label cost.…”