Multi-Instance Learning(MIL) aims to learn the mapping between a bag of instances and the bag-level label. Therefore, the relationships among instances are very important for learning the mapping. In this paper, we propose an MIL algorithm based on a graph built by structural relationship among instances within a bag. Then, Graph Convolutional Network(GCN) and the graph-attention mechanism are used to learn bag-embedding. In the task of medical image classification, our GCN-based MIL algorithm makes full use of the structural relationships among patches(instances) in an original image space domain, and experimental results verify that our method is more suitable for handling medical high-resolution images. We also verify experimentally that the proposed method achieves better results than previous methods on five bechmark MIL datasets and four medical image datasets.

翻译：多因子学习(MIL) 旨在学习一包实例和包层标签之间的映射。 因此, 实例之间的关系对于学习映射非常重要 。 在本文中, 我们根据一个包中各实例之间的结构关系构建的图表提出一个MIL算法。 然后, 图形革命网络(GCN) 和图形注意机制被用来学习包装。 在医学图像分类的任务中, 我们基于 GCN 的 MIL 算法充分利用了原始图像空间域中各补丁( Insistances) 之间的结构关系, 实验结果可以证实我们的方法更适合处理高分辨率的医疗图像。 我们还通过实验来核实, 拟议的方法在5个赫马克 MIL 数据集和 4个医疗图像数据集上取得了比以往方法更好的效果。