Objectives Develop and evaluate the performance of deep learning and linear regression
cascade algorithms for automated assessment of the image layout and position of chest …

Purpose To automate diagnostic chest radiograph imaging quality control (lung inclusion at
all four edges, patient rotation, and correct inspiration) using convolutional neural network …

Purpose: The authors previously proposed an image‐based technique [Y. Lin et al. Med.
Phys. 39, 7019–7031 (2012)] to assess the perceptual quality of clinical chest radiographs …

Ensuring correct radiograph view labeling is important for machine learning algorithm
development and quality control of studies obtained from multiple facilities. The purpose of …

Background Deep learning has the potential to augment the use of chest radiography in
clinical radiology, but challenges include poor generalizability, spectrum bias, and difficulty …

Background: Chest X-ray (CXR) imaging is the most common examination; however, no
automatic quality assurance (QA) system using deep learning (DL) has been established for …

Purpose The purpose of this study was to determine whether a proposed suite of objective
image quality metrics for digital chest radiographs is useful for monitoring image quality in a …

The quality of chest radiographs is a practical issue because deviations from quality
standards cost radiologists' time, may lead to misdiagnosis and hold legal risks. Automatic …

Background Chest computed tomography (CT) image quality impacts radiologists'
diagnoses. Pre-diagnostic image quality assessment is essential but labor-intensive and …

Purpose To assess the ability of convolutional neural networks (CNNs) to enable high-
performance automated binary classification of chest radiographs. Materials and Methods In …