Diabetic retinopathy (DR) caused by diabetes occurs as a result of changes in the retinal vessels and causes visual impairment. Microaneurysms (MAs) are the early clinical signs of DR, whose timely diagnosis can help detecting DR in the early stages of its development. It has been observed that MAs are more common in the inner retinal layers compared to the outer retinal layers in eyes suffering from DR. Optical coherence tomography (OCT) is a noninvasive imaging technique that provides a cross-sectional view of the retina, and it has been used in recent years to diagnose many eye diseases. As a result, this paper attempts to identify areas with MA from normal areas of the retina using OCT images. This work is done using the dataset collected from FA and OCT images of 20 patients with DR. In this regard, firstly fluorescein angiography (FA) and OCT images were registered. Then, the MA and normal areas were separated, and the features of each of these areas were extracted using the Bag of Features (BOF) approach with the Speeded-Up Robust Feature (SURF) descriptor. Finally, the classification process was performed using a multilayer perceptron network. For each of the criteria of accuracy, sensitivity, specificity, and precision, the obtained results were 96.33%, 97.33%, 95.4%, and 95.28%, respectively. Utilizing OCT images to detect MAs automatically is a new idea, and the results obtained as preliminary research in this field are promising.
In recent decades, due to the groundbreaking improvements in machine vision, many daily tasks are performed by computers. One of these tasks is multiple-vehicle tracking, which is widely used in different areas such as video surveillance and traffic monitoring. This paper focuses on introducing an efficient novel approach with acceptable accuracy. This is achieved through an efficient appearance and motion model based on the features extracted from each object. For this purpose, two different approaches have been used to extract features, i.e. features extracted from a deep neural network, and traditional features. Then the results from these two approaches are compared with state-of-the-art trackers. The results are obtained by executing the methods on the UA-DETRACK benchmark. The first method led to 58.9% accuracy while the second method caused up to 15.9%. The proposed methods can still be improved by extracting more distinguishable features.
Graph neural network (GNN) is an emerging field of research that tries to generalize deep learning architectures to work with non-Euclidean data. Nowadays, combining deep reinforcement learning (DRL) with GNN for graph-structured problems, especially in multi-agent environments, is a powerful technique in modern deep learning. From the computational point of view, multi-agent environments are inherently complex, because future rewards depend on the joint actions of multiple agents. This chapter tries to examine different types of applying GNN and DRL techniques in the most common representations of multi-agent problems and their challenges. In general, the fusion of GNN and DRL can be addressed from two different points of view. First, GNN is used to influence the DRL performance and improve its formulation. Here, GNN is applied in relational DRL structures such as multi-agent and multi-task DRL. Second, DRL is used to improve the application of GNN. From this viewpoint, DRL can be used for a variety of purposes including neural architecture search and improving the explanatory power of GNN predictions.
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