  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.
