ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging

Medical image segmentation is one of the major challenges addressed by machine learning methods. Yet, deep learning methods profoundly depend on a huge amount of annotated data which is time-consuming and costly. Though semi-supervised learning methods approach this problem by leveraging an abundant amount of unlabeled data along with a small amount of labeled data in the training process. Recently, MixUp regularizer [32] has been successfully introduced to semi-supervised learning methods showing superior performance [3]. MixUp augments the model with new data points through linear interpolation of the data at the input space. In this paper, we argue that this option is limited, instead, we propose ROAM, a random layer mixup, which encourages the network to be less confident for interpolated data points at randomly selected space. Hence, avoids over-fitting and enhances the generalization ability. We validate our method on publicly available datasets on whole-brain image segmentation (MALC) achieving state-of-the-art results in fully supervised (89.8%) and semi-supervised (87.2%) settings with relative improvement up to 2.75% and 16.73%, respectively.