Intelligent medical diagnosis has shown remarkable progress based on the large-scale datasets with precise annotations. However, fewer labeled images are available due to significantly expensive cost for annotating data by experts. To fully exploit the easily available unlabeled data, we propose a novel Spatio-Temporal Structure Consistent (STSC) learning framework. Specifically, a gram matrix is derived to combine the spatial structure consistency and temporal structure consistency together. This gram matrix captures the structural similarity among the representations of different training samples. At the spatial level, our framework explicitly enforces the consistency of structural similarity among different samples under perturbations. At the temporal level, we consider the consistency of the structural similarity in different training iterations by digging out the stable sub-structures in a relation graph. Experiments on two medical image datasets (i.e., ISIC 2018 challenge and ChestX-ray14) show that our method outperforms state-of-the-art SSL methods. Furthermore, extensive qualitative analysis on the Gram matrices and heatmaps by Grad-CAM are presented to validate the effectiveness of our method.
翻译:智能医学诊断显示,根据具有准确说明说明的大规模数据集,取得了显著进展;然而,由于专家说明数据的费用昂贵,贴有标签的图像较少;为了充分利用容易获得的无标签数据,我们提议了一个全新的Spatio-Teoporal 结构一致性(STSC)学习框架;具体地说,为了将空间结构的一致性和时间结构的一致性结合起来,将得出一个语法矩阵。这个语法矩阵反映了不同培训样本在结构上的相似性。在空间层面,我们的框架明确强制执行在扰动下不同样本的结构相似性的一致性。在时间层面,我们考虑通过在关系图中挖掘稳定的亚结构来在不同培训中的结构相似性。关于两个医学图像数据集(即ISIC 2018挑战与ChestX-ray14)的实验表明,我们的方法超越了科学科学模型方法的状态。此外,我们还提出了关于格拉德-CAM的格模矩阵和热谱图的广泛的定性分析,以验证我们的方法的有效性。</s>