Certainty Pooling for Multiple Instance Learning

• URL: http://arxiv.org/abs/2008.10548v1
• Date: Mon, 24 Aug 2020 16:38:46 GMT
• Title: Certainty Pooling for Multiple Instance Learning
• Authors: Jacob Gildenblat, Ido Ben-Shaul, Zvi Lapp, and Eldad Klaiman
• Abstract summary: We present a novel pooling operator called textbfCertainty Pooling which incorporates the model certainty into bag predictions. Our method outperforms other methods in both bag level and instance level prediction, especially when only small training sets are available.
• Score: 0.6299766708197883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple Instance Learning is a form of weakly supervised learning in which the data is arranged in sets of instances called bags with one label assigned per bag. The bag level class prediction is derived from the multiple instances through application of a permutation invariant pooling operator on instance predictions or embeddings. We present a novel pooling operator called \textbf{Certainty Pooling} which incorporates the model certainty into bag predictions resulting in a more robust and explainable model. We compare our proposed method with other pooling operators in controlled experiments with low evidence ratio bags based on MNIST, as well as on a real life histopathology dataset - Camelyon16. Our method outperforms other methods in both bag level and instance level prediction, especially when only small training sets are available. We discuss the rationale behind our approach and the reasons for its superiority for these types of datasets.

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