Diagnostic imaging features of the most commonly performed types of bariatric surgery, which are gastric banding, sleeve gastrectomy, and Roux-en-Y gastric bypass, are reviewed as well as imaging diagnosis of their complications. Although upper gastrointestinal series remains the first-line imaging test for assessing postoperative anatomy and complications, the important role of multidetector computed tomography in diagnosis of serious complications is highlighted.
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http://dx.doi.org/10.1053/j.sult.2013.04.005 | 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
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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December 2024
Institute of Computer Science, Zurich University of Applied Sciences, 8400 Winterthur, Switzerland.
Simultaneous localization and mapping (SLAM) techniques can be used to navigate the visually impaired, but the development of robust SLAM solutions for crowded spaces is limited by the lack of realistic datasets. To address this, we introduce InCrowd-VI, a novel visual-inertial dataset specifically designed for human navigation in indoor pedestrian-rich environments. Recorded using Meta Aria Project glasses, it captures realistic scenarios without environmental control.
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