  There are different multiple instance learning (MIL) pooling filters used in
MIL models. In this paper, we study the effect of different MIL pooling filters
on the performance of MIL models in real world MIL tasks. We designed a neural
network based MIL framework with 5 different MIL pooling filters: `max',
`mean', `attention', `distribution' and `distribution with attention'. We also
formulated 5 different MIL tasks on a real world lymph node metastases dataset.
We found that the performance of our framework in a task is different for
different filters. We also observed that the performances of the five pooling
filters are also different from task to task. Hence, the selection of a correct
MIL pooling filter for each MIL task is crucial for better performance.
Furthermore, we noticed that models with `distribution' and `distribution with
attention' pooling filters consistently perform well in almost all of the
tasks. We attribute this phenomena to the amount of information captured by
`distribution' based pooling filters. While point estimate based pooling
filters, like `max' and `mean', produce point estimates of distributions,
`distribution' based pooling filters capture the full information in
distributions. Lastly, we compared the performance of our neural network model
with `distribution' pooling filter with the performance of the best MIL methods
in the literature on classical MIL datasets and our model outperformed the
others.
