AIMC Topic: Quality Control

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Machine-learning-based quality control of contractility of cultured human-induced pluripotent stem-cell-derived cardiomyocytes.

Biochemical and biophysical research communications
The precise and early assessment of cardiotoxicity is fundamental to bring forward novel drug candidates to the pharmaceutical market and to avoid their withdrawal from the market. Recent preclinical studies have attempted to use human-induced plurip...

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...

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning.

Computational intelligence and neuroscience
We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weat...

Artificial intelligence in diagnostic imaging: impact on the radiography profession.

The British journal of radiology
The arrival of artificially intelligent systems into the domain of medical imaging has focused attention and sparked much debate on the role and responsibilities of the radiologist. However, discussion about the impact of such technology on the radio...

Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy.

The Journal of investigative dermatology
In vivo reflectance confocal microscopy (RCM) enables clinicians to examine lesions' morphological and cytological information in epidermal and dermal layers while reducing the need for biopsies. As RCM is being adopted more widely, the workflow is e...

A fast and scalable method for quality assurance of deformable image registration on lung CT scans using convolutional neural networks.

Medical physics
PURPOSE: To develop and evaluate a method to automatically identify and quantify deformable image registration (DIR) errors between lung computed tomography (CT) scans for quality assurance (QA) purposes.

Computationally efficient deep neural network for computed tomography image reconstruction.

Medical physics
PURPOSE: Deep neural network-based image reconstruction has demonstrated promising performance in medical imaging for undersampled and low-dose scenarios. However, it requires large amount of memory and extensive time for the training. It is especial...

PET image denoising using unsupervised deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Image quality of positron emission tomography (PET) is limited by various physical degradation factors. Our study aims to perform PET image denoising by utilizing prior information from the same patient. The proposed method is based on unsup...

Predicting gamma passing rates for portal dosimetry-based IMRT QA using machine learning.

Medical physics
PURPOSE: Intensity-modulated radiation therapy (IMRT) quality assurance (QA) measurements are routinely performed prior to treatment delivery to verify dose calculation and delivery accuracy. In this work, we applied a machine learning-based approach...

Accelerating cardiovascular model building with convolutional neural networks.

Medical & biological engineering & computing
The objective of this work is to reduce the user effort required for 2D segmentation when building patient-specific cardiovascular models using the SimVascular cardiovascular modeling software package. The proposed method uses a fully convolutional n...