Learning dynamic weights for an ensemble of deep models applied to medical imaging classification

An ensemble of deep models is commonly used to provide more robust and accurate performance for medical image classification. A drawback of the most common ensemble aggregation operators is that they give the same importance to all models in the ensemble. As a consequence, they cannot identify weak models that may negatively influence the ensemble performance. In this work, we propose a new method based on the Dirichlet distribution and Mahalanobis distance to learn dynamic weights to an ensemble of deep learning models. Through this method, it is possible to reduce the influence of weak models for each new sample evaluated by the ensemble and perform online ensemble pruning. We evaluate this method for an ensemble of six well-known deep models applied to four medical imaging datasets. The experiments show that our method achieves the best balanced accuracy for 2 out of 4 datasets and increases the confidence of the ensemble predictions.