  Semi-Supervised classification and segmentation methods have been widely
investigated in medical image analysis. Both approaches can improve the
performance of fully-supervised methods with additional unlabeled data.
However, as a fundamental task, semi-supervised object detection has not gained
enough attention in the field of medical image analysis. In this paper, we
propose a novel Semi-Supervised Medical image Detector (SSMD). The motivation
behind SSMD is to provide free yet effective supervision for unlabeled data, by
regularizing the predictions at each position to be consistent. To achieve the
above idea, we develop a novel adaptive consistency cost function to regularize
different components in the predictions. Moreover, we introduce heterogeneous
perturbation strategies that work in both feature space and image space, so
that the proposed detector is promising to produce powerful image
representations and robust predictions. Extensive experimental results show
that the proposed SSMD achieves the state-of-the-art performance at a wide
range of settings. We also demonstrate the strength of each proposed module
with comprehensive ablation studies.
