Studying The Effect of MIL Pooling Filters on MIL Tasks
This work addresses the practical problem of selecting effective MIL pooling filters for medical imaging tasks, though it is incremental as it systematically compares existing filters rather than introducing new ones.
The paper investigates how different multiple instance learning (MIL) pooling filters affect model performance across five real-world MIL tasks on a lymph node metastases dataset, finding that distribution-based filters consistently outperform others and that filter selection is crucial for optimal results. Their neural network model with a distribution pooling filter outperformed existing methods on classical MIL datasets.
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.