  Semantic segmentation has a wide array of applications ranging from
medical-image analysis, scene understanding, autonomous driving and robotic
navigation. This work deals with medical image segmentation and in particular
with accurate polyp detection and segmentation during colonoscopy examinations.
Several convolutional neural network architectures have been proposed to
effectively deal with this task and with the problem of segmenting objects at
different scale input. The basic architecture in image segmentation consists of
an encoder and a decoder: the first uses convolutional filters to extract
features from the image, the second is responsible for generating the final
output. In this work, we compare some variant of the DeepLab architecture
obtained by varying the decoder backbone. We compare several decoder
architectures, including ResNet, Xception, EfficentNet, MobileNet and we
perturb their layers by substituting ReLU activation layers with other
functions. The resulting methods are used to create deep ensembles which are
shown to be very effective. Our experimental evaluations show that our best
ensemble produces good segmentation results by achieving high evaluation scores
with a dice coefficient of 0.884, and a mean Intersection over Union (mIoU) of
0.818 for the Kvasir-SEG dataset. To improve reproducibility and research
efficiency the MATLAB source code used for this research is available at
GitHub: https://github.com/LorisNanni.
