Domain Generalization for Prostate Cancer Detection in MRI

Modern computer-aided detection/diagnosis (CAD) based on deep learning algorithms achieve high results in detection of prostate cancer in magnetic resonance imaging (MRI). However, the performance of these algorithms drop when the testing cases are taken from a different domain (i.e. samples acquired using a different MRI scanner). In this research, we have investigated the performances of the state-of-the-art domain generalization techniques beginning from the simple solutions like histogram matching to the more advanced deep learning based models like CycleGAN. We do not introduce any new novel method in this study rather we have reapplied the current state-of-the-art techniques and compared the performances. From our experimental results, we have deduced that simple solutions are not adequate to capture the complexity of medical images and hence fail to obtain domain generalization. We have to rely on advanced techniques that take into account not just the intensity information but also the spatial information to achieve our goal.