The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well as abnormality detection and segmentation. However, even the most advanced methods require a huge and diverse amount of data to generalize. Because in realistic clinical scenarios, data acquisition and annotation is expensive, deep learning models trained on small and unrepresentative data tend to outperform when deployed in data that differs from the one used for training (e.g data from different scanners). In this work, we proposed a domain adaptation methodology to alleviate this problem in segmentation models. Our approach takes advantage of the properties of adversarial domain adaptation and consistency training to achieve more robust adaptation. Using two datasets with white matter hyperintensities (WMH) annotations, we demonstrated that the proposed method improves model generalization even in corner cases where individual strategies tend to fail.

翻译：在医学图像分析中加入深层次学习导致在诸如疾病分类以及异常检测和分解等若干应用中制定了最先进的战略,但是,即使是最先进的方法也需要大量多样的数据才能概括化。因为在现实的临床假设情景中,数据获取和批注费用昂贵,因此,在小型和无代表性数据方面受过培训的深层次学习模型在与培训数据(例如来自不同扫描仪的数据)不同的数据中部署时,往往优于成绩。在这项工作中,我们建议了一种领域适应方法,以缓解分解模型中的这一问题。我们的方法利用对抗性域适应和一致性培训的特性,以实现更强有力的适应。我们利用两种含有白物质超密度说明的数据集,我们证明拟议的方法甚至在个别战略往往失败的角落中也改善了模型的统化。