We propose a novel semi-supervised learning method to leverage unlabeled data alongside minimal annotated data and improve medical imaging classification performance in realistic scenarios with limited labeling budgets to afford data annotations. Our method introduces distance correlation to minimize correlations between feature representations from different views of the same image encoded with non-coupled deep neural networks architectures. In addition, it incorporates a data-driven graph-attention based regularization strategy to model affinities among images within the unlabeled data by exploiting their inherent relational information in the feature space. We conduct extensive experiments on four medical imaging benchmark data sets involving X-ray, dermoscopic, magnetic resonance, and computer tomography imaging on single and multi-label medical imaging classification scenarios. Our experiments demonstrate the effectiveness of our method in achieving very competitive performance and outperforming several state-of-the-art semi-supervised learning methods. Furthermore, they confirm the suitability of distance correlation as a versatile dependence measure and the benefits of the proposed graph-attention based regularization for semi-supervised learning in medical imaging analysis.

中文翻译：

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通过距离相关最小化和图注意正则化的半监督医学图像分类

我们提出了一种新颖的半监督学习方法，利用未标记的数据和最少的注释数据，并在标签预算有限的情况下提高现实场景中的医学成像分类性能，以提供数据注释。我们的方法引入了距离相关性，以最小化使用非耦合深度神经网络架构编码的同一图像的不同视图的特征表示之间的相关性。此外，它还结合了基于数据驱动的图注意的正则化策略，通过利用特征空间中固有的关系信息来对未标记数据中的图像之间的亲和力进行建模。我们在涉及 X 射线、皮肤镜、磁共振和计算机断层扫描成像的四个医学成像基准数据集上，在单标签和多标签医学成像分类场景上进行了广泛的实验。我们的实验证明了我们的方法在实现非常有竞争力的性能方面的有效性，并且优于几种最先进的半监督学习方法。此外，他们证实了距离相关性作为通用依赖性度量的适用性，以及所提出的基于图注意的正则化对于医学成像分析中半监督学习的好处。