Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/S0140-6736(19)30004-2 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Towards practical and privacy-preserving CNN inference service for cloud-based medical imaging analysis: A homomorphic encryption-based approach.
Comput Methods Programs Biomed
January 2025
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China.
Background And Objective: Cloud-based Deep Learning as a Service (DLaaS) has transformed biomedicine by enabling healthcare systems to harness the power of deep learning for biomedical data analysis. However, privacy concerns emerge when sensitive user data must be transmitted to untrusted cloud servers. Existing privacy-preserving solutions are hindered by significant latency issues, stemming from the computational complexity of inner product operations in convolutional layers and the high communication costs of evaluating nonlinear activation functions.
View Article and Find Full Text PDFHow Reproducible Are the Ultrasound Features of Adenomyosis Defined by the Revised MUSA Consensus?
J Clin Med
January 2025
Derby Fertility Unit, Royal Derby Hospital, University Hospital of Derby and Burton, Derby DE22 3NE, UK.
: The aim of this study is to assess the inter- and intra-observer reproducibility of the identification of direct and indirect ultrasonographic features of adenomyosis as defined by the revised Morphological Uterus Sonographic Assessment (MUSA) consensus (2022). : A cohort of 74 women, aged 18 to 45, were recruited from the recurrent miscarriage and general gynaecology clinic at a university-based fertility centre. All the participants underwent 2D and 3D transvaginal Ultrasound scan (TVS) examination in the late follicular and early luteal phase.
View Article and Find Full Text PDFA Low-Power General Matrix Multiplication Accelerator with Sparse Weight-and-Output Stationary Dataflow.
Micromachines (Basel)
January 2025
Research Center for Novel Computing Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou 311100, China.
General matrix multiplication (GEMM) in machine learning involves massive computation and data movement, which restricts its deployment on resource-constrained devices. Although data reuse can reduce data movement during GEMM processing, current approaches fail to fully exploit its potential. This work introduces a sparse GEMM accelerator with a weight-and-output stationary (WOS) dataflow and a distributed buffer architecture.
View Article and Find Full Text PDFA 4-Week Mobile App-Based Telerehabilitation Program vs Conventional In-Person Rehabilitation in Older Adults With Sarcopenia: Randomized Controlled Trial.
J Med Internet Res
January 2025
Department of Rehabilitation Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Sarcopenia is closely associated with a poor quality of life and mortality, and its prevention and treatment represent a critical area of research. Resistance training is an effective treatment for older adults with sarcopenia. However, they often face challenges when receiving traditional rehabilitation treatments at hospitals.
View Article and Find Full Text PDFOnline analysis of Amazon's soils through reflectance spectroscopy and cloud computing can support policies and the sustainable development.
J Environ Manage
January 2025
Geotechnologies in Soil Sciences Research Group - GeoCiS, Department of Soil Science, Luiz de Queiroz College of Agriculture - Esalq, University of São Paulo - USP, Piracicaba, São Paulo, Brazil. Electronic address:
Analyzing soil in large and remote areas such as the Amazon River Basin (ARB) is unviable when it is entirely performed by wet labs using traditional methods due to the scarcity of labs and the significant workforce requirements, increasing costs, time, and waste. Remote sensing, combined with cloud computing, enhances soil analysis by modeling soil from spectral data and overcoming the limitations of traditional methods. We verified the potential of soil spectroscopy in conjunction with cloud-based computing to predict soil organic carbon (SOC) and particle size (sand, silt, and clay) content from the Amazon region.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!