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End-to-End Multi-Scale Adaptive Remote Sensing Image Dehazing Network.
Sensors (Basel)
January 2025
School of Computer Science, Northeast Electric Power University, Jilin 132012, China.
Satellites frequently encounter atmospheric haze during imaging, leading to the loss of detailed information in remote sensing images and significantly compromising image quality. This detailed information is crucial for applications such as Earth observation and environmental monitoring. In response to the above issues, this paper proposes an end-to-end multi-scale adaptive feature extraction method for remote sensing image dehazing (MSD-Net).
View Article and Find Full Text PDFDevelopment and validation of a model to identify polycystic ovary syndrome in the French national administrative health database.
BMC Med Res Methodol
January 2025
Department of Gynecology-Obstetric and Reproductive Medicine, AP-HM, La Conception University teaching Hospital, 147 Boulevard Baille, Marseille, 13005, France.
Background: We aimed to develop and validate an algorithm for identifying women with polycystic ovary syndrome (PCOS) in the French national health data system.
Methods: Using data from the French national health data system, we applied the International Classification of Diseases (ICD-10) related diagnoses E28.2 for PCOS among women aged 18 to 43 years in 2021.
Deep learning algorithms enable MRI-based scapular morphology analysis with values comparable to CT-based assessments.
Sci Rep
January 2025
Department of Orthopaedic Surgery and Traumatology, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.
Scapular morphological attributes show promise as prognostic indicators of retear following rotator cuff repair. Current evaluation techniques using single-slice magnetic-resonance imaging (MRI) are, however, prone to error, while more accurate computed tomography (CT)-based three-dimensional techniques, are limited by cost and radiation exposure. In this study we propose deep learning-based methods that enable automatic scapular morphological analysis from diagnostic MRI despite the anisotropic resolution and reduced field of view, compared to CT.
View Article and Find Full Text PDFBritish Society of Gastroenterology National Evaluation of Colonoscopy Quality: Findings from the National Endoscopy Database.
Gastrointest Endosc
January 2025
Population Health Sciences Institute, Newcastle University Centre for Cancer, Newcastle University, Newcastle-upon-Tyne, UK, NE2 4AX; North Tees and Hartlepool NHS Foundation Trust, TS19 8PE.
Background And Aims: Analysis of national colonoscopy quality using automatically uploaded data from a national database, including exploring performance variation.
Methods: Data on all colonoscopies performed in the UK 01/03/2019-29/02/2020 and recorded in the National Endoscopy Database were analysed. Unadjusted key performance indicators were calculated and proportions of endoscopists achieving national standards were determined.
Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data.
Sensors (Basel)
December 2024
School of Biological and Environmental Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool L3 3AF, UK.
Camera traps offer enormous new opportunities in ecological studies, but current automated image analysis methods often lack the contextual richness needed to support impactful conservation outcomes. Integrating vision-language models into these workflows could address this gap by providing enhanced contextual understanding and enabling advanced queries across temporal and spatial dimensions. Here, we present an integrated approach that combines deep learning-based vision and language models to improve ecological reporting using data from camera traps.
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