AIMC Topic: Radiography, Thoracic

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Deep Convolutional Neural Networks for Chest Diseases Detection.

Journal of healthcare engineering Aug 1, 2018
Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many me...
Respiratory Tract Diseases Deep Learning Humans Radiographic Image Interpretation, Computer-Assisted Algorithms Radiography, Thoracic
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Peripheral bronchial identification on chest CT using unsupervised machine learning.

International journal of computer assisted radiology and surgery Jun 13, 2018
PURPOSE: To automatically identify small- to medium-diameter bronchial segments distributed throughout the lungs.
Humans Algorithms Tomography, X-Ray Computed Unsupervised Machine Learning Cross-Sectional Studies Radiography, Thoracic Bronchi Imaging, Three-Dimensional
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Machine learning "red dot": open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification.

Clinical radiology Jun 10, 2018
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 prioritisation.
Radiography, Thoracic Cloud Computing Humans Neural Networks, Computer Sensitivity and Specificity Machine Learning Diagnosis, Differential Datasets as Topic
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Automatic classification of radiological reports for clinical care.

Artificial intelligence in medicine Jun 7, 2018
Radiological reporting generates a large amount of free-text clinical narratives, a potentially valuable source of information for improving clinical care and supporting research. The use of automatic techniques to analyze such reports is necessary t...
Humans Machine Learning Tomography, X-Ray Computed Information Storage and Retrieval Radiography, Thoracic Interatrial Block Decision Trees Natural Language Processing Data Mining
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Prediction of radiographic abnormalities by the use of bag-of-features and convolutional neural networks.

Veterinary journal (London, England : 1997) May 29, 2018
This study evaluated the feasibility of bag-of-features (BOF) and convolutional neural networks (CNN) for computer-aided detection in distinguishing normal from abnormal radiographic findings. Computed thoracic radiographs of dogs were collected. For...
Lung Diseases Dogs Dog Diseases Heart Diseases Radiography, Thoracic Sensitivity and Specificity Animals Diagnosis, Computer-Assisted Neural Networks, Computer Reproducibility of Results
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Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder.

Biomedical engineering online May 23, 2018
OBJECTIVE: In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, hig...
ROC Curve Image Processing, Computer-Assisted Triage Machine Learning Lung Automation Signal-To-Noise Ratio Radiography, Thoracic Humans
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Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks.

IEEE transactions on medical imaging Apr 27, 2018
The mathematical problem for electrical impedance tomography (EIT) is a highly nonlinear ill-posed inverse problem requiring carefully designed reconstruction procedures to ensure reliable image generation. D-bar methods are based on a rigorous mathe...
Humans Algorithms Computer Simulation Lung Deep Learning Phantoms, Imaging Fourier Analysis Tomography Electric Impedance Radiography, Thoracic
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Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans.

Physics in medicine and biology Apr 5, 2018
Automatic localization of organs and other structures in medical images is an important preprocessing step that can improve and speed up other algorithms such as organ segmentation, lesion detection, and registration. This work presents an efficient ...
Humans Neural Networks, Computer Tomography, X-Ray Computed Image Processing, Computer-Assisted Abdomen Radiography, Thoracic
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Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort.

Journal of biomedical informatics Nov 23, 2017
We proposed an unsupervised hybrid method - Intelligent Word Embedding (IWE) that combines neural embedding method with a semantic dictionary mapping technique for creating a dense vector representation of unstructured radiology reports. We applied I...
Pulmonary Embolism Natural Language Processing Humans Machine Learning Radiographic Image Interpretation, Computer-Assisted Semantics Neural Networks, Computer Tomography, X-Ray Computed Radiography, Thoracic Predictive Value of Tests
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Deep Learning to Classify Radiology Free-Text Reports.

Radiology Nov 13, 2017
Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) ...
Machine Learning Algorithms Tomography, X-Ray Computed Humans Natural Language Processing Neural Networks, Computer Reproducibility of Results Pulmonary Embolism Sensitivity and Specificity Radiography, Thoracic ROC Curve
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