|
Prostate cancer (PCa) is the second most commonly diagnosed cancer worldwide among men. In spite of it, its current diagnostic pathway is substantially hampered by over-diagnosis of indolent lesions and under-detection of aggressive ones. Imaging techniques like magnetic resonance imaging (MRI) have proven to add additional value to the current diagnostic practices, but they rely on specialized training and can be time-intensive. Deep learning (DL) has arisen as an alternative to automatize tasks such as MRI analysis. Nevertheless, its success relies on large amounts of annotated data which are rarely available in the medical domain. Existing work tackling data scarcity commonly relies on ImageNet pre-training, which is sub-optimal due to the existing gap between the training and the task domain. We propose a generative self-supervised learning (SSL) approach to alleviate such issues. We show that by making use of an auto-encoder architecture and by applying different patch-level transformations such as pixel intensity or occlusion transformations to T2w MRI slices and then trying to recover the original T2w slice we are able to learn robust medical visual representations that are domain-specific. Furthermore, we show the usefulness of our approach by making use of the representations as an initialization method for PCa lesion classification downstream task. Following, we show how our method outperforms ImageNet initialization and how the performance gap increases as the amount of the available labeled data decreases. Furthermore, we provide a detailed sensitivity analysis of the different pixel manipulation transformations and their effect on the downstream task performance.
|
