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http://dx.doi.org/10.1523/JNEUROSCI.0971-11.2011 | DOI Listing |
NPJ Digit Med
January 2025
Institut Curie, CNRS UMR168, PSL University, Sorbonne University, Paris, 75005, France.
Generating synthetic data from medical records is a complex task intensified by patient privacy concerns. In recent years, multiple approaches have been reported for the generation of synthetic data, however, limited attention was given to jointly evaluate the quality and the privacy of the generated data. The quality and privacy of synthetic data stem from multivariate associations across variables, which cannot be assessed by comparing univariate distributions with the original data.
View Article and Find Full Text PDFSci Rep
January 2025
College of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang, 550025, China.
Deep learning has achieved significant success in the field of defect detection; however, challenges remain in detecting small-sized, densely packed parts under complex working conditions, including occlusion and unstable lighting conditions. This paper introduces YOLOv8-n as the core network to propose VEE-YOLO, a robust and high-performance defect detection model. Firstly, GSConv was introduced to enhance feature extraction in depthwise separable convolution and establish the VOVGSCSP module, emphasizing feature reusability for more effective feature engineering.
View Article and Find Full Text PDFNat Med
January 2025
O'Neill Institute for National & Global Health Law, Georgetown University, Washington, DC, USA.
Sci Rep
January 2025
Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou, 450002, China.
Identification and diagnosis of tobacco diseases are prerequisites for the scientific prevention and control of these ailments. To address the limitations of traditional methods, such as weak generalization and sensitivity to noise in segmenting tobacco leaf lesions, this study focused on four tobacco diseases: angular leaf spot, brown spot, wildfire disease, and frog eye disease. Building upon the Unet architecture, we developed the Multi-scale Residual Dilated Segmentation Model (MD-Unet) by enhancing the feature extraction module and integrating attention mechanisms.
View Article and Find Full Text PDFAnn Nucl Med
January 2025
Department of Radiology, The University of Osaka Hospital, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
Objective: Data-driven respiratory gating (DDG) has recently been introduced to improve image quality in the PET portion of PET/CT examinations. The latest DDG system does not require any external equipment or extended examination time. In this study, we investigated the effects of the new DDG system on the visualization and quantification of breast and upper abdominal cancers, comparing the results with those obtained using the standard free-breathing (STD) PET protocol.
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