  Multi-instance learning (MIL) is a form of weakly supervised learning where a
single class label is assigned to a bag of instances while the instance-level
labels are not available. Training classifiers to accurately determine the bag
label and instance labels is a challenging but critical task in many practical
scenarios, such as computational histopathology. Recently, MIL models fully
parameterized by neural networks have become popular due to the high
flexibility and superior performance. Most of these models rely on attention
mechanisms that assign attention scores across the instance embeddings in a bag
and produce the bag embedding using an aggregation operator. In this paper, we
proposed a dual-stream maximum self-attention MIL model (DSMIL) parameterized
by neural networks. The first stream deploys a simple MIL max-pooling while the
top-activated instance embedding is determined and used to obtain
self-attention scores across instance embeddings in the second stream.
Different from most of the previous methods, the proposed model jointly learns
an instance classifier and a bag classifier based on the same instance
embeddings. The experiments results show that our method achieves superior
performance compared to the best MIL methods and demonstrates state-of-the-art
performance on benchmark MIL datasets.
