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A 3D decoupling Alzheimer's disease prediction network based on structural MRI.
Health Inf Sci Syst
December 2025
School of Mathematics and Computing, University of Southern Queensland, 487-535 West Street, Toowoomba, QLD 4350 Australia.
Purpose: This paper aims to develop a three-dimensional (3D) Alzheimer's disease (AD) prediction method, thereby bettering current predictive methods, which struggle to fully harness the potential of structural magnetic resonance imaging (sMRI) data.
Methods: Traditional convolutional neural networks encounter pressing difficulties in accurately focusing on the AD lesion structure. To address this issue, a 3D decoupling, self-attention network for AD prediction is proposed.
Enhancing semantic segmentation for autonomous vehicle scene understanding in indian context using modified CANet model.
MethodsX
June 2025
Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Nigdi, Pune 411044, India.
Recent advancements in artificial intelligence (AI) have increased interest in intelligent transportation systems, particularly autonomous vehicles. Safe navigation in traffic-heavy environments requires accurate road scene segmentation, yet traditional computer vision methods struggle with complex scenarios. This study emphasizes the role of deep learning in improving semantic segmentation using datasets like the Indian Driving Dataset (IDD), which presents unique challenges in chaotic road conditions.
View Article and Find Full Text PDFOpportunities for Improving the Long-term Management of Keratoconus Patients.
J Contact Lens Res Sci
July 2024
Illinois College of Optometry, Chicago.
Background And Objective: This study determined whether practitioners specializing in keratoconus (KC) adhere to published guidelines for disease management and to what extent comorbid conditions of dry eye, contact lens tolerance, and psychological consequences of KC are formally assessed as part of long-term management.
Materials And Methods: This cross-sectional study used an IRB-approved, Internet-based, REDCap platform. Descriptive statistics are presented.
Localized Drug Delivery in Different Gastrointestinal Cancers: Navigating Challenges and Advancing Nanotechnological Solutions.
Int J Nanomedicine
January 2025
Department of Pharmacology, Faculty of Pharmacy, Delta University for Science and Technology, Gamasa, 11152, Egypt.
Different types of cancers affect the gastrointestinal tract (GIT), starting from the oral cavity and extending to the colon. In general, most of the current research focuses on the systemic delivery of the therapeutic agents, which leads to undesired side effects and a limited enhancement in the therapeutic outcomes. As a result, localized delivery within gastrointestinal (GI) cancers is favorable in overcoming these limitations.
View Article and Find Full Text PDFA fast monocular 6D pose estimation method for textureless objects based on perceptual hashing and template matching.
Front Robot AI
January 2025
Department of Materials and Production, Aalborg University, Aalborg, Denmark.
Object pose estimation is essential for computer vision applications such as quality inspection, robotic bin picking, and warehouse logistics. However, this task often requires expensive equipment such as 3D cameras or Lidar sensors, as well as significant computational resources. Many state-of-the-art methods for 6D pose estimation depend on deep neural networks, which are computationally demanding and require GPUs for real-time performance.
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