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Calibration

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Using decision curve analysis to benchmark performance of a magnetic resonance imaging-based deep learning model for prostate cancer risk assessment.

European radiology
OBJECTIVES: To benchmark the performance of a calibrated 3D convolutional neural network (CNN) applied to multiparametric MRI (mpMRI) for risk assessment of clinically significant prostate cancer (csPCa) using decision curve analysis (DCA).

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot.

Journal of visualized experiments : JoVE
This protocol presents a method for manufacturing, control, and evaluation of the performance of a soft robot that can climb inclined flat surfaces with slopes of up to 84°. The manufacturing method is valid for the fast pneunet bending actuators in ...

Spectral resolution of quaternary components in a sinus and congestion mixture; Multivariate algorithms to approach extremes of concentration levels.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Sinus and congestion mixture of three drugs and an impurity was studied for their spectral resolution using four multivariate algorithms. The studied drugs present in extremes of low and high concentrations. Low concentration levels of phenylephrine ...

Channel width optimized neural networks for liver and vessel segmentation in liver iron quantification.

Computers in biology and medicine
INTRODUCTION: MRI T2* relaxometry protocols are often used for Liver Iron Quantification in patients with hemochromatosis. Several methods exist to semi-automatically segment parenchyma and exclude vessels for this calculation.

A probabilistic approach for calibration time reduction in hybrid EEG-fTCD brain-computer interfaces.

Biomedical engineering online
BACKGROUND: Generally, brain-computer interfaces (BCIs) require calibration before usage to ensure efficient performance. Therefore, each BCI user has to attend a certain number of calibration sessions to be able to use the system. However, such cali...

NormAE: Deep Adversarial Learning Model to Remove Batch Effects in Liquid Chromatography Mass Spectrometry-Based Metabolomics Data.

Analytical chemistry
Untargeted metabolomics based on liquid chromatography-mass spectrometry is affected by nonlinear batch effects, which cover up biological effects, result in nonreproducibility, and are difficult to be calibrate. In this study, we propose a novel dee...

Simultaneous ultra-trace quantitative colorimetric determination of antidiabetic drugs based on gold nanoparticles aggregation using multivariate calibration and neural network methods.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
In this study, a simple and rapid method was investigated for the simultaneous ultra-trace colorimetric determination of Metformin (MET) and Sitagliptin (STG) based on the aggregation of gold nanoparticles (AuNPs). The Morphology and size distributio...

Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy.

Medical image analysis
There are two challenges associated with the interpretability of deep learning models in medical image analysis applications that need to be addressed: confidence calibration and classification uncertainty. Confidence calibration associates the class...

Learning Mobile Manipulation through Deep Reinforcement Learning.

Sensors (Basel, Switzerland)
Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that d...