  Semi-supervised learning has the potential to improve the data-efficiency of
training data-hungry deep neural networks, which is especially important for
medical image analysis tasks where labeled data is scarce. In this work, we
present a simple semi-supervised learning method for lesion segmentation tasks
based on the ideas of cut-paste augmentation and consistency regularization. By
exploiting the mask information available in the labeled data, we synthesize
partially labeled samples from the unlabeled images so that the usual
supervised learning objective (e.g., binary cross entropy) can be applied.
Additionally, we introduce a background consistency term to regularize the
training on the unlabeled background regions of the synthetic images. We
empirically verify the effectiveness of the proposed method on two public
lesion segmentation datasets, including an eye fundus photograph dataset and a
brain CT scan dataset. The experiment results indicate that our method achieves
consistent and superior performance over other self-training and
consistency-based methods without introducing sophisticated network components.
