In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence
research. It is also one of the most popular scientific research trends now-a-days. Deep …

Brain extraction from magnetic resonance imaging (MRI) is crucial for many neuroimaging
workflows. Current methods demonstrate good results on non-enhanced T1-weighted …

We are interested in inferring object segmentation by leveraging only object class
information, and by considering only minimal priors on the object segmentation task. This …

This paper presents a novel change detection approach for synthetic aperture radar images
based on deep learning. The approach accomplishes the detection of the changed and …

In solving change detection problem, unsupervised methods are usually preferred to their
supervised counterparts due to the difficulty of producing labelled data. Nevertheless, in this …

Automatic segmentation of blood vessels in fundus images is of great importance as eye
diseases as well as some systemic diseases cause observable pathologic modifications. It is …

Aiming at improving the classification accuracy with limited numbers of labeled pixels in
polarimetric synthetic aperture radar (PolSAR) image classification task, this paper presents …

Background and objective Diabetic retinopathy is one of the leading disabling chronic
diseases and one of the leading causes of preventable blindness in developed world. Early …

The ancient term" Deep Learning" was first introduced to Machine Learning by Dechter
(1986), and to Artificial Neural Networks (NNs) by Aizenberg et al (2000). Subsequently it …