Deep ensembles in bioimage segmentation

TL;DR

This paper introduces a novel deep ensemble approach for bioimage segmentation, combining diverse CNN models with a new loss function to improve accuracy in polyp and skin lesion segmentation tasks.

Contribution

It proposes a new ensemble method using different backbone networks and a combined loss function, enhancing segmentation performance over existing methods.

Findings

Improved segmentation accuracy on polyp and skin datasets.

Effective use of diverse CNN architectures in ensemble.

Open-source code available for reproducibility.

Abstract

Semantic segmentation consists in classifying each pixel of an image by assigning it to a specific label chosen from a set of all the available ones. During the last few years, a lot of attention shifted to this kind of task. Many computer vision researchers tried to apply autoencoder structures to develop models that can learn the semantics of the image as well as a low-level representation of it. In an autoencoder architecture, given an input, an encoder computes a low dimensional representation of the input that is then used by a decoder to reconstruct the original data. In this work, we propose an ensemble of convolutional neural networks (CNNs). In ensemble methods, many different models are trained and then used for classification, the ensemble aggregates the outputs of the single classifiers. The approach leverages on differences of various classifiers to improve the performance of the whole system. Diversity among the single classifiers is enforced by using different loss functions. In particular, we present a new loss function that results from the combination of Dice and Structural Similarity Index. The proposed ensemble is implemented by combining different backbone networks using the DeepLabV3+ and HarDNet environment. The proposal is evaluated through an extensive empirical evaluation on two real-world scenarios: polyp and skin segmentation. All the code is available online at https://github.com/LorisNanni.