Ensemble of deep learning models with surrogate-based optimization for medical image segmentation

Deep Neural Networks (DNNs) have created a breakthrough in medical image analysis in recent years. Because clinical applications of automated medical analysis are required to be reliable, robust and accurate, it is necessary to devise effective DNNs based models for medical applications. In this paper, we propose an ensemble framework of DNNs for the problem of medical image segmentation with a note that combining multiple models can obtain better results compared to each constituent one. We introduce an effective combining strategy for individual segmentation models based on swarm intelligence, which is a family of optimization algorithms inspired by biological processes. The problem of expensive computational time of the optimizer during the objective function evaluation is relieved by using a surrogate-based method. We train a surrogate on the objective function information of some populations and then use it to predict the objective values of each candidate in the subsequent populations. Experiments run on a number of public datasets indicate that our framework achieves competitive results within reasonable computation time.

Automated summary

Deep Neural Networks (DNNs) have made significant advances in medical image analysis. In order to meet the requirements of reliable, robust, and accurate clinical applications, effective DNN-based models are needed. This paper proposes an ensemble framework of DNNs for medical image segmentation, and introduces swarm intelligence and surrogate-based methods to enhance model performance and reduce computation time.