We report the case of a 38-year-old man with transient perivascular inflammation of the carotid artery syndrome that occurred in the course of covid-19. We describe for the first-time multimodal imaging features of the perivascular changes surrounding the carotid artery, and long-term follow-up by ultrasound. The imaging features observed on ultrasound, angiography-CT, MRI and FDG-Pet scan support the hypothesis of the inflammatory nature of the perivascular tissue thickening. The ultrasound follow-up confirmed the spontaneous resolution of the lesion, leaving on site some residual changes as sequelae. The good knowledge of the imaging features reported herein helps to recognize this entity in patients with covid-19.
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http://dx.doi.org/10.1016/j.radcr.2021.12.005 | DOI Listing |
Sensors (Basel)
December 2024
NUS-ISS, National University of Singapore, Singapore 119615, Singapore.
The attention mechanism is essential to (CNN) vision backbones used for sensing and imaging systems. Conventional attention modules are designed heuristically, relying heavily on empirical tuning. To tackle the challenge of designing attention mechanisms, this paper proposes a novel probabilistic attention mechanism.
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December 2024
School of Computer and Artificial Intelligence, Wuhan Textile Unversity, Wuhan 430200, China.
Currently, fabric defect detection methods predominantly rely on CNN models. However, due to the inherent limitations of CNNs, such models struggle to capture long-distance dependencies in images and fail to accurately detect complex defect features. While Transformers excel at modeling long-range dependencies, their quadratic computational complexity poses significant challenges.
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December 2024
Facultad de Ingeniería, Pontificia Universidad Javeriana, Bogotá 110231, Colombia.
Cutaneous leishmaniasis is a parasitic disease that poses significant diagnostic challenges due to the variability of results and reliance on operator expertise. This study addresses the development of a system based on machine learning algorithms to detect spp. parasite in direct smear microscopy images, contributing to the diagnosis of cutaneous leishmaniasis.
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December 2024
Shanghai Research Institute of Microelectronics, Peking University, Shanghai 201203, China.
Despite the accuracy and robustness attained in the field of object tracking, algorithms based on Siamese neural networks often over-rely on information from the initial frame, neglecting necessary updates to the template; furthermore, in prolonged tracking situations, such methodologies encounter challenges in efficiently addressing issues such as complete occlusion or instances where the target exits the frame. To tackle these issues, this study enhances the SiamRPN algorithm by integrating the convolutional block attention module (CBAM), which enhances spatial channel attention. Additionally, it integrates the kernelized correlation filters (KCFs) for enhanced feature template representation.
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December 2024
Division of Computer Science & Artificial Intelligence, Dongguk University, Seoul 04620, Republic of Korea.
Anomaly detection is critical in safety-sensitive fields, but faces challenges from scarce abnormal data and costly expert labeling. Time series anomaly detection is relatively challenging due to its reliance on sequential data, which imposes high computational and memory costs. In particular, it is often composed of real-time collected data that tends to be noisy, making preprocessing an essential step.
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