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.

翻译：在医学图像分析中,对半监视分类和分解方法进行了广泛调查。两种方法都可以改进全监视方法的性能,增加未贴标签的数据。但是,作为一项基本任务,半监视物体的探测在医学图像分析领域没有引起足够的注意。在本文中,我们建议采用一个新的半监视医学图像探测器(SSMD)。SSMD背后的动机是,通过对每个位置的预测进行常规化,为未贴标签数据提供免费有效的监督。为了实现上述设想,我们开发了一个新的适应一致性成本功能,以使不同组成部分在预测中正规化。此外,我们引入了在地貌空间和图像空间都起作用的多变的扰动战略,因此拟议的探测器有希望产生强有力的图像表现和强有力的预测。广泛的实验结果显示,拟议的SSMD在各种环境中都实现了最先进的性能。我们还展示了每个拟议模块的强度,并进行了全面的模拟研究。