AIMC Topic: Algorithms

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A Deep Residual U-Net Algorithm for Automatic Detection and Quantification of Ascites on Abdominopelvic Computed Tomography Images Acquired in the Emergency Department: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: Detection and quantification of intra-abdominal free fluid (ie, ascites) on computed tomography (CT) images are essential processes for finding emergent or urgent conditions in patients. In an emergency department, automatic detection and...

E-Commerce Picture Text Recognition Information System Based on Deep Learning.

Computational intelligence and neuroscience
For the accuracy requirements of commodity image detection and classification, the FPN network is improved by DPFM ablation and RFM, so as to improve the detection accuracy of commodities by the network. At the same time, in view of the narrowing of ...

Pruning Growing Self-Organizing Map Network for Human Physical Activity Identification.

Journal of healthcare engineering
Human physical activity identification based on wearable sensors is of great significance to human health analysis. A large number of machine learning models have been applied to human physical activity identification and achieved remarkable results....

A multilayer perceptron neural network approach for the solution of hyperbolic telegraph equations.

Network (Bristol, England)
Neural networks have been extensively used for solving differential equations in the past, but they rely mostly on computationally expensive gradient-based numerical optimization procedure for solving differential equations. In this work, we are intr...

Technologies bringing young Zebrafish from a niche field to the limelight.

SLAS technology
Fundamental life science and pharmaceutical research are continually striving to provide physiologically relevant context for their biological studies. Zebrafish present an opportunity for high-content screening (HCS) to bring a true in vivo model sy...

Verte-Box: A Novel Convolutional Neural Network for Fully Automatic Segmentation of Vertebrae in CT Image.

Tomography (Ann Arbor, Mich.)
Due to the complex shape of the vertebrae and the background containing a lot of interference information, it is difficult to accurately segment the vertebrae from the computed tomography (CT) volume by manual segmentation. This paper proposes a conv...

What is morally at stake when using algorithms to make medical diagnoses? Expanding the discussion beyond risks and harms.

Theoretical medicine and bioethics
In this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis. To date, discussions about machine learning in this context have focused on problems that can be m...

Sensor Data Fusion for a Mobile Robot Using Neural Networks.

Sensors (Basel, Switzerland)
Mobile robots must be capable to obtain an accurate map of their surroundings to move within it. To detect different materials that might be undetectable to one sensor but not others it is necessary to construct at least a two-sensor fusion scheme. W...

AI Based Monitoring of Different Risk Levels in COVID-19 Context.

Sensors (Basel, Switzerland)
COVID-19 was responsible for devastating social, economic, and political effects all over the world. Although the health authorities imposed restrictions provided relief and assisted with trying to return society to normal life, it is imperative to m...

A Two-Stage Approach to Important Area Detection in Gathering Place Using a Novel Multi-Input Attention Network.

Sensors (Basel, Switzerland)
An important area in a gathering place is a region attracting the constant attention of people and has evident visual features, such as a flexible stage or an open-air show. Finding such areas can help security supervisors locate the abnormal regions...