Abstract
With the rise of Transformers, Vision Transformers (ViTs) have become a new standard in visual recognition. This has led to the development of numerous architectures with diverse designs and applications. This survey identifies 22 key ViT and hybrid CNN–ViT models, along with 5 top Convolutional Neural Network (CNN) models. These were selected based on their new architecture, relevance to benchmarks, and overall impact. The models are organised using a defined taxonomy formed by CNN-based, pure Transformer-based, and hybrid architectures. We analyse their main components, training methods, and computational features, while assessing performance using reported results on standard benchmarks such as ImageNet and CIFAR, along with our training and fine-tuning evaluations on specific imaging datasets. In addition to accuracy, we look at real-world deployment issues by analysing the trade-offs between accuracy and efficiency in embedded, mobile, and clinical settings. The results indicate that modern CNNs are still very competitive in limited-resource environments, while advanced ViT variants perform well after large-scale pretraining, especially in areas with high variability. Hybrid CNN–ViT architectures, on the other hand, tend to offer the best balance between accuracy, data efficiency, and computational cost. This survey establishes a consolidated benchmark and reference framework for understanding the evolution, capabilities, and practical applicability of contemporary vision architectures. © © 2026. Published by Elsevier Ltd.

Abstract
The segmentation of the foveal avascular zone (FAZ) in optical coherence tomography angiography (OCTA) images plays a crucial role in diagnosing and monitoring ocular diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). However, accurate FAZ segmentation remains challenging due to image quality and variability. This paper provides a comprehensive review of FAZ segmentation techniques, including traditional image processing methods and recent deep learning-based approaches. We propose two novel deep learning methodologies: a multitask learning framework that integrates vessel and FAZ segmentation, and a conditionally trained network that employs vessel-aware loss functions. The performance of the proposed methods was evaluated on the OCTA-500 dataset using the Dice coefficient, Jaccard index, 95% Hausdorff distance, and average symmetric surface distance. Experimental results demonstrate that the multitask segmentation framework outperforms existing state-of-the-art methods, achieving superior FAZ boundary delineation and segmentation accuracy. The conditionally trained network also improves upon standard U-Net-based approaches but exhibits limitations in refining the FAZ contours.

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With the growing popularity of DevOps culture among companies and the corresponding increase in Microservices architecture development-both known to boost productivity and efficiency in software development-an increasing number of organizations are aiming to integrate them. Implementing DevOps culture and best practices can be challenging, but it is increasingly important as software applications become more robust and complex, and performance is considered essential by end users. By following the Design Science Research methodology, this paper proposes an iterative framework that closely follows the recommended DevOps practices, validated with the assistance of expert interviews, for implementing DevOps practices into Microservices architecture software development, while also offering a series of tools that serve as a base guideline for anyone following this framework, in the form of a theoretical use case. Therefore, this paper provides organizations with a guideline for adapting DevOps and offers organizations already using this methodology a framework to potentially enhance their established practices.