  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.
