AIMC Topic: Radiography, Thoracic

Clear Filters Showing 421 to 430 of 591 articles

Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT.

Medical image analysis Jul 10, 2019
Classification of benign-malignant lung nodules on chest CT is the most critical step in the early detection of lung cancer and prolongation of patient survival. Despite their success in image classification, deep convolutional neural networks (DCNNs...
Radiographic Image Interpretation, Computer-Assisted Supervised Machine Learning Datasets as Topic Precancerous Conditions Radiography, Thoracic Algorithms Tomography, X-Ray Computed Lung Neoplasms Solitary Pulmonary Nodule Humans
View on PubMed DOI

Deep Learning to Assess Long-term Mortality From Chest Radiographs.

JAMA network open Jul 3, 2019
IMPORTANCE: Chest radiography is the most common diagnostic imaging test in medicine and may also provide information about longevity and prognosis.
Aged Male Cardiovascular Diseases Middle Aged Prognosis Humans Female Neural Networks, Computer Risk Factors Lung Diseases Risk Assessment Clinical Trials as Topic Radiography, Thoracic Deep Learning Thorax Mass Screening
View on PubMed DOI

Feasibility of Natural Language Processing-Assisted Auditing of Critical Findings in Chest Radiology.

Journal of the American College of Radiology : JACR Jun 21, 2019
OBJECTIVE: Time-sensitive communication of critical imaging findings like pneumothorax or pulmonary embolism to referring physicians is essential for patient safety. The definitive communication is the radiology free-text report. Quality assurance in...
United States Natural Language Processing Feasibility Studies Quality Improvement Sensitivity and Specificity Male Pilot Projects Radiography, Thoracic Academic Medical Centers Automation Radiology Information Systems Humans Machine Learning Tomography, X-Ray Computed Female Research Design Retrospective Studies
View on PubMed DOI

Effect of augmented datasets on deep convolutional neural networks applied to chest radiographs.

Clinical radiology Jun 10, 2019
AIM: To evaluate the effect of augmented training datasets in a deep convolutional neural network (DCNN) used for detecting abnormal chest radiographs.
Humans Neural Networks, Computer Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic
View on PubMed DOI

An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks.

Computer methods and programs in biomedicine Jun 6, 2019
BACKGROUND AND OBJECTIVE: Chest X-ray (CXR) is one of the most used imaging techniques for detection and diagnosis of pulmonary diseases. A critical component in any computer-aided system, for either detection or diagnosis in digital CXR, is the auto...
Radiography, Thoracic Humans Image Processing, Computer-Assisted Sensitivity and Specificity Databases, Factual Tuberculosis, Pulmonary Lung Reproducibility of Results Pattern Recognition, Automated Diagnosis, Computer-Assisted Neural Networks, Computer Algorithms
View on PubMed DOI

Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis.

Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society May 2, 2019
BACKGROUND: The aim of this study was to evaluate the hypothesis that a deep convolutional neural network (DCNN) model could facilitate automated Brasfield scoring of chest radiographs (CXRs) for patients with cystic fibrosis (CF), performing similar...
Humans Neural Networks, Computer Tomography, X-Ray Computed Female Research Design Prognosis Pediatrics Severity of Illness Index Radiography, Thoracic Deep Learning Reproducibility of Results Male Child Lung Cystic Fibrosis
View on PubMed DOI

Dense networks with relative location awareness for thorax disease identification.

Medical physics Apr 14, 2019
PURPOSE: Chest X-ray is one of the most common examinations for diagnosing heart and lung diseases. Due to the existing of a large number of clinical cases, many automated diagnosis algorithms based on chest X-ray images have been proposed. To our kn...
Thoracic Diseases Lung Diseases Pattern Recognition, Automated Radiography, Thoracic Diagnosis, Computer-Assisted Humans Predictive Value of Tests Neural Networks, Computer Algorithms Radiographic Image Interpretation, Computer-Assisted Databases, Factual
View on PubMed DOI

Learning to Reconstruct Computed Tomography Images Directly From Sinogram Data Under A Variety of Data Acquisition Conditions.

IEEE transactions on medical imaging Apr 11, 2019
Computed tomography (CT) is widely used in medical diagnosis and non-destructive detection. Image reconstruction in CT aims to accurately recover pixel values from measured line integrals, i.e., the summed pixel values along straight lines. Provided ...
Deep Learning Image Processing, Computer-Assisted Abdomen Coronary Vessels Humans Head Tomography, X-Ray Computed Radiography, Thoracic Phantoms, Imaging
View on PubMed DOI

Separation of bones from soft tissue in chest radiographs: Anatomy-specific orientation-frequency-specific deep neural network convolution.

Medical physics Mar 28, 2019
PURPOSE: Lung nodules that are missed by radiologists as well as by computer-aided detection (CAD) systems mostly overlap with ribs and clavicles. Removing the bony structures would result in better visualization of undetectable lesions. Our purpose ...
Clavicle Humans Radiography, Thoracic Neural Networks, Computer Organ Specificity Ribs Image Processing, Computer-Assisted
View on PubMed DOI

Cardiac Rhythm Device Identification Using Neural Networks.

JACC. Clinical electrophysiology Mar 27, 2019
OBJECTIVES: This paper reports the development, validation, and public availability of a new neural network-based system which attempts to identify the manufacturer and even the model group of a pacemaker or defibrillator from a chest radiograph.
Pacemaker, Artificial Image Processing, Computer-Assisted Radiography, Thoracic Humans Thorax Neural Networks, Computer Databases, Factual
View on PubMed DOI
Popular Topics
Recent Journals