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

Studying The Effect of MIL Pooling Filters on MIL Tasks