Weighted Ensemble of Deep Learning Models based on Comprehensive Learning Particle Swarm Optimization for Medical Image Segmentation

Truong Dang,Carlos Francisco Moreno-Garcia,Eyad Elyan,Tien Thanh Nguyen,John Mccall

doi:10.1109/cec45853.2021.9504929

Truong Dang, Carlos Francisco Moreno-Garcia + Show 3 more

Open Access

https://doi.org/10.1109/cec45853.2021.9504929

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Publication Date: Jun 28, 2021
Citations: 13	License type: other-oa

Affiliation: Robert Gordon University

Abstract

In recent years, deep learning has rapidly become a method of choice for segmentation of medical images. Deep neural architectures such as UNet and FPN have achieved high performances on many medical datasets. However, medical image analysis algorithms are required to be reliable, robust, and accurate for clinical applications which can be difficult to achieve for some single deep learning methods. In this study, we introduce an ensemble of classifiers for semantic segmentation of medical images. The ensemble of classifiers here is a set of various deep learning-based classifiers, aiming to achieve better performance than using a single classifier. We propose a weighted ensemble method in which the weighted sum of segmentation outputs by classifiers is used to choose the final segmentation decision. We use a swarm intelligence algorithm namely Comprehensive Learning Particle Swarm Optimization to optimize the combining weights. Dice coefficient, a popular performance metric for image segmentation, is used as the fitness criteria. Experiments conducted on some medical datasets of the CAMUS competition on cardiographic image segmentation show that our method achieves better results than both the constituent segmentation models and the reported model of the CAMUS competition.

Highlights

Image segmentation is the process of partitioning an input image into regions which correspond to different objects or parts of an object
A solution to these difficulties is to combine multiple deep learning models trained on medical image datasets which would guarantee better predictions compared to using individual deep models
It is recognized that the equal contribution of classifiers may downgrade the performance of Ensemble of Classifiers (EoC) because classifiers perform differently on a particular dataset and some classifiers need to contribute more than the others

Summary

Introduction

Image segmentation is the process of partitioning an input image into regions which correspond to different objects or parts of an object. It is well known that localization and interpolation of anatomical structures in medical images, which is a key step in radiological workflow, was performed by handcrafting image filters to extract anatomical signatures [15] This is a timeconsuming process and requires expert knowledge. This poses a problem for creating deep models for medical imaging which are robust against overfitting Another problem is that the process of training deep neural networks using popular optimizers such as Stochastic Gradient Descent (SGD) generally require much manual tuning of optimization parameters such as learning rates and convergence criteria [41]. Pacheco et al [29] performed ensemble selection and pruning of deep learning classifiers by learning the Dirichlet distribution of the output probabilities and optimizing the weights dynamically using a loss function based on Mahalanobis distance

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