AI Medical Compendium Topic:
Radiographic Image Interpretation, Computer-Assisted

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Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning.

Journal of X-ray science and technology Jan 1, 2019
BACKGROUND: Deep learning has made spectacular achievements in analysing natural images, but it faces challenges for medical applications partly due to inadequate images.
Humans Databases, Factual Tomography, X-Ray Computed Lung Neoplasms Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Lung Deep Learning ROC Curve
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Breast mass detection and diagnosis using fused features with density.

Journal of X-ray science and technology Jan 1, 2019
BACKGROUND: The morbidity of breast cancer has been increased in these years and ranked the first of all female diseases. Computer-aided diagnosis techniques for mammograms can help radiologists find early breast lesions. In mammograms, the degree of...
Humans Adult Breast Neoplasms Middle Aged ROC Curve Databases, Factual Female Aged Breast Unsupervised Machine Learning Mammography Radiographic Image Interpretation, Computer-Assisted
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Expert knowledge-infused deep learning for automatic lung nodule detection.

Journal of X-ray science and technology Jan 1, 2019
BACKGROUND: Computer aided detection (CADe) of pulmonary nodules from computed tomography (CT) is crucial for early diagnosis of lung cancer. Self-learned features obtained by training datasets via deep learning have facilitated CADe of the nodules. ...
Multiple Pulmonary Nodules Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Knowledge Bases Sensitivity and Specificity Deep Learning Lung Neoplasms Humans Neural Networks, Computer Tomography, X-Ray Computed
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Evaluation of an AI-Based Detection Software for Acute Findings in Abdominal Computed Tomography Scans: Toward an Automated Work List Prioritization of Routine CT Examinations.

Investigative radiology Jan 1, 2019
OBJECTIVE: The aim of this study was to test the diagnostic performance of a deep learning-based triage system for the detection of acute findings in abdominal computed tomography (CT) examinations.
Sensitivity and Specificity Humans Radiographic Image Interpretation, Computer-Assisted Artificial Intelligence Radiology Information Systems Algorithms Radiography, Abdominal Tomography, X-Ray Computed
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3D deep learning for detecting pulmonary nodules in CT scans.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
OBJECTIVE: To demonstrate and test the validity of a novel deep-learning-based system for the automated detection of pulmonary nodules.
Radiographic Image Interpretation, Computer-Assisted ROC Curve Multiple Pulmonary Nodules Solitary Pulmonary Nodule Humans Tomography, X-Ray Computed Sensitivity and Specificity Lung Diagnosis, Computer-Assisted Deep Learning
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Prediction of Response to Stereotactic Radiosurgery for Brain Metastases Using Convolutional Neural Networks.

Anticancer research Sep 1, 2018
BACKGROUND: It is unclear whether radiomic phenotypes of brain metastases (BM) are related to radiation therapy prognosis. This study assessed whether a convolutional neural network (CNN)-based radiomics model which learned computer tomography (CT) i...
Radiosurgery Treatment Outcome Predictive Value of Tests Area Under Curve Radiographic Image Interpretation, Computer-Assisted Adolescent Time Factors ROC Curve Reproducibility of Results Aged Aged, 80 and over Young Adult Decision Support Techniques Adult Tomography, X-Ray Computed Humans Brain Neoplasms Clinical Decision-Making Male Neural Networks, Computer Female Middle Aged Machine Learning
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Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium.

Circulation. Cardiovascular imaging Jun 1, 2018
BACKGROUND: Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necess...
Humans Coronary Stenosis Prospective Studies Fractional Flow Reserve, Myocardial Coronary Artery Disease Retrospective Studies Predictive Value of Tests Coronary Vessels Severity of Illness Index Female Coronary Angiography Reproducibility of Results Asia Radiographic Image Interpretation, Computer-Assisted Aged Deep Learning Computed Tomography Angiography Male United States Europe Middle Aged
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Deep Learning Computed Tomography: Learning Projection-Domain Weights From Image Domain in Limited Angle Problems.

IEEE transactions on medical imaging Jun 1, 2018
In this paper, we present a new deep learning framework for 3-D tomographic reconstruction. To this end, we map filtered back-projection-type algorithms to neural networks. However, the back-projection cannot be implemented as a fully connected layer...
Humans Algorithms Tomography, X-Ray Computed Deep Learning Radiographic Image Interpretation, Computer-Assisted
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[Clinical analysis of spectrum CT imaging reducing metal artifacts of oral and maxillofacial region].

Shanghai kou qiang yi xue = Shanghai journal of stomatology Dec 1, 2017
PURPOSE: To assess the capability of monochromatic energy images of gemstone spectral imaging(GSI) by using spectral CT in reducing metal artifacts of oral and maxillofacial region.
Radiographic Image Interpretation, Computer-Assisted Humans Dental Implants Tomography, X-Ray Computed Artifacts Metals
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Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

Technology and health care : official journal of the European Society for Engineering and Medicine Jul 20, 2017
BACKGROUND: Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer.
Mammography Algorithms Radiographic Image Interpretation, Computer-Assisted Female Humans Support Vector Machine Breast Breast Neoplasms Fuzzy Logic Calcinosis Models, Statistical
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