Mohamed Amroune

Khadija Rais, Mohamed Amroune, Abdelmadjid Benmachiche et al.

Variational autoencoder (VAE) is one of the most common techniques in the field of medical image generation, where this architecture has shown advanced researchers in recent years and has developed into various architectures. VAE has advantages including improving datasets by adding samples in smaller datasets and in datasets with imbalanced classes, and this is how data augmentation works. This paper provides a comprehensive review of studies on VAE in medical imaging, with a special focus on their ability to create synthetic images close to real data so that they can be used for data augmentation. This study reviews important architectures and methods used to develop VAEs for medical images and provides a comparison with other generative models such as GANs on issues such as image quality, and low diversity of generated samples. We discuss recent developments and applications in several medical fields highlighting the ability of VAEs to improve segmentation and classification accuracy.

Khadija Rais, Mohamed Amroune, Mohamed Yassine Haouam

Medical image analysis suffers from a lack of labeled data due to several challenges including patient privacy and lack of experts. Although some AI models only perform well with large amounts of data, we will move to data augmentation where there is a solution to improve the performance of our models and increase the dataset size through traditional or advanced techniques. In this paper, we evaluate the effectiveness of data augmentation techniques on two different medical image datasets. In the first step, we applied some transformation techniques to the skin cancer dataset containing benign and malignant classes. Then, we trained the convolutional neural network (CNN) on the dataset before and after augmentation, which significantly improved test accuracy from 90.74% to 96.88% and decreased test loss from 0.7921 to 0.1468 after augmentation. In the second step, we used the Mixup technique by mixing two random images and their corresponding masks using the retina and blood vessels dataset, then we trained the U-net model and obtained the Dice coefficient which increased from 0 before augmentation to 0.4163 after augmentation. The result shows the effect of using data augmentation to increase the dataset size on the classification and segmentation performance.

Mohamed Amroune, Pierre Jean Charrel, Nacereddine Zarour et al.

To reuse one or several existing systems in order to develop a complex system is a common practice in software engineering. This approach can be justified by the fact that it is often difficult for a single Information System (IS) to accomplish all the requested tasks. So, one solution is to combine many different ISs and make them collaborate in order to realize these tasks. We proposed an approach named AspeCiS (An Aspect-oriented Approach to Develop a Cooperative Information System) to develop a Cooperative Information System from existing ISs by using their artifacts such as existing requirements, and design. AspeCiS covers the three following phases: (i) discovery and analysis of Cooperative Requirements, (ii) design of Cooperative Requirements models, and (iii) preparation of the implementation phase. The main issue of AspeCiS is the definition of Cooperative Requirements using the Existing Requirements and Additional Requirements, which should be composed with Aspectual Requirements. We earlier studied how to elicit the Cooperative Requirements in AspeCiS (phase of discovery and analysis of Cooperative Requirements in AspeCiS) . We study here the second phase of AspeCiS (design of Cooperative Requirements models), by the way of a model weaving process. This process uses so-called AspeCiS Weaving Metamodel, and it weaves Existing and Additional Requirements models to realize Cooperative Requirements models.

Mohamed Amroune, Jean Michel Inglebert, Nacereddine Zarour et al.

The development of a Cooperative Information System (CIS) becomes more and more complex, new challenges arise for managing this complexity. So, the aspect paradigm is regarded as a promising software development technique which can reduce the complexity and cost of developing large software systems. This opportunity can be used to develop a CIS able to support the interconnection of organizations information systems in order to ensure a common global service and to support the tempo of change in the business world that is increasing at an exponential level. We previously proposed an approach named AspeCiS (An Aspect-oriented Approach to Develop a Cooperative Information System) to develop a Cooperative Information System from existing Information Systems by using their artifacts such as existing requirements, and design. In this approach we have studied how to elicit CIS Requirements called Cooperative Requirements in AspeCiS. In this paper we propose a weaving process to define these requirements by reusing existing requirements and new aspectual requirements that we define to modify these requirements in order to be reused.