@inproceedings{e103e0858a5f47d4901ce719427388af,
title = "Attention-based deep multiple instance learning",
abstract = "Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the bag label where the bag label probability is fully parameterized by neural networks. Furthermore, we propose a neural network-based permutation-invariant aggregation operator that corresponds to the attention mechanism. Notably, an application of the proposed attention-based operator provides insight into the contribution of each instance to the bag label. We show empirically that our approach achieves comparable performance to the best MIL methods on benchmark MIL datasets and it outperforms other methods on a MNIST-based MIL dataset and two real-life histopathology datasets without sacrificing interpretability.",
author = "Maximilian Ilse and Tomczak, {Jakub M.} and Max Welling",
year = "2018",
month = jan,
day = "1",
language = "English",
series = "35th International Conference on Machine Learning, ICML 2018",
publisher = "International Machine Learning Society (IMLS)",
pages = "3376--3391",
editor = "Jennifer Dy and Andreas Krause",
booktitle = "35th International Conference on Machine Learning, ICML 2018",
note = "35th International Conference on Machine Learning, ICML 2018 ; Conference date: 10-07-2018 Through 15-07-2018",
}