ArXiv Preprint
Multiple Instance Learning (MIL) is a weakly supervised learning paradigm
that is becoming increasingly popular because it requires less labeling effort
than fully supervised methods. This is especially interesting for areas where
the creation of large annotated datasets remains challenging, as in medicine.
Although recent deep learning MIL approaches have obtained state-of-the-art
results, they are fully deterministic and do not provide uncertainty
estimations for the predictions. In this work, we introduce the Attention
Gaussian Process (AGP) model, a novel probabilistic attention mechanism based
on Gaussian Processes for deep MIL. AGP provides accurate bag-level predictions
as well as instance-level explainability, and can be trained end-to-end.
Moreover, its probabilistic nature guarantees robustness to overfitting on
small datasets and uncertainty estimations for the predictions. The latter is
especially important in medical applications, where decisions have a direct
impact on the patient's health. The proposed model is validated experimentally
as follows. First, its behavior is illustrated in two synthetic MIL experiments
based on the well-known MNIST and CIFAR-10 datasets, respectively. Then, it is
evaluated in three different real-world cancer detection experiments. AGP
outperforms state-of-the-art MIL approaches, including deterministic deep
learning ones. It shows a strong performance even on a small dataset with less
than 100 labels and generalizes better than competing methods on an external
test set. Moreover, we experimentally show that predictive uncertainty
correlates with the risk of wrong predictions, and therefore it is a good
indicator of reliability in practice. Our code is publicly available.
Arne Schmidt, Pablo Morales-Álvarez, Rafael Molina
2023-02-08
