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

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 implement a tool for real time image quality feedback for chest radiographs into
the clinical routine and to evaluate the effect of the system on the image quality of the …

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

Chest X-rays are among the most common modalities in medical imaging. Technical flaws of
these images, such as over-or under-exposure or wrong positioning of the patients can …

AIM To evaluate the effect of augmented training datasets in a deep convolutional neural
network (DCNN) used for detecting abnormal chest radiographs. MATERIALS AND …

Aim To develop a machine learning-based model for the binary classification of chest
radiography abnormalities, to serve as a retrospective tool in guiding clinician reporting …

Introduction Chest radiographs are the most performed radiographic procedure, but
suboptimal technical factors can impact clinical interpretation. A deep learning model was …

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 …