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Semantic segmentation consists in classifying each pixel of an image byassigning 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 todevelop models that can learn the semantics of the image as well as a low-levelrepresentation of it. In an autoencoder architecture, given an input, anencoder computes a low dimensional representation of the input that is thenused by a decoder to reconstruct the original data. In this work, we propose anensemble of convolutional neural networks (CNNs). In ensemble methods, manydifferent models are trained and then used for classification, the ensembleaggregates the outputs of the single classifiers. The approach leverages ondifferences of various classifiers to improve the performance of the wholesystem. Diversity among the single classifiers is enforced by using differentloss functions. In particular, we present a new loss function that results fromthe combination of Dice and Structural Similarity Index. The proposed ensembleis implemented by combining different backbone networks using the DeepLabV3+and HarDNet environment. The proposal is evaluated through an extensiveempirical evaluation on two real-world scenarios: polyp and skin segmentation.All the code is available online at https://github.com/LorisNanni.

Deep ensembles in bioimage segmentation