Kernel Self-Attention in Deep Multiple Instance Learning
This addresses MIL challenges in domains like medical imaging by improving aggregation methods, though it appears incremental as it builds on existing attention-based MIL approaches.
The authors tackled the problem of dependencies between instances in multiple instance learning (MIL) for tasks like medical image analysis, by introducing a Self-Attention Attention-based MIL Pooling (SA-AbMILP) operation, which outperformed other models on datasets including MNIST, histological, microbiological, and retinal databases.
Not all supervised learning problems are described by a pair of a fixed-size input tensor and a label. In some cases, especially in medical image analysis, a label corresponds to a bag of instances (e.g. image patches), and to classify such bag, aggregation of information from all of the instances is needed. There have been several attempts to create a model working with a bag of instances, however, they are assuming that there are no dependencies within the bag and the label is connected to at least one instance. In this work, we introduce Self-Attention Attention-based MIL Pooling (SA-AbMILP) aggregation operation to account for the dependencies between instances. We conduct several experiments on MNIST, histological, microbiological, and retinal databases to show that SA-AbMILP performs better than other models. Additionally, we investigate kernel variations of Self-Attention and their influence on the results.