AIMC Topic: Radiography, Thoracic

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Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images.

Journal of healthcare engineering Aug 9, 2017
Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depe...
Neural Networks, Computer Software Algorithms Humans ROC Curve Image Processing, Computer-Assisted Lung Neoplasms Solitary Pulmonary Nodule Radiography, Thoracic Sensitivity and Specificity Diagnosis, Computer-Assisted Deep Learning Tomography, X-Ray Computed Reproducibility of Results
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Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.

Radiology Apr 24, 2017
Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified HIPAA-compliant datasets were used in this study that were exempted from revi...
Humans Neural Networks, Computer ROC Curve Tuberculosis, Pulmonary Algorithms Radiography, Thoracic Retrospective Studies Sensitivity and Specificity
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A doubly robust approach for cost-effectiveness estimation from observational data.

Statistical methods in medical research Feb 27, 2017
Estimation of common cost-effectiveness measures, including the incremental cost-effectiveness ratio and the net monetary benefit, is complicated by the need to account for informative censoring and inherent skewness of the data. In addition, since t...
Cost-Benefit Analysis Databases, Factual Machine Learning Diagnostic Tests, Routine Tomography, X-Ray Computed Propensity Score Algorithms Radiography, Thoracic Lung Neoplasms Observation
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Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.

Radiological physics and technology Feb 16, 2017
Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I descr...
Radiography, Thoracic Machine Learning Image Processing, Computer-Assisted Lung Neoplasms Humans
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Lung nodule classification using deep feature fusion in chest radiography.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Nov 12, 2016
Lung nodules are small, round, or oval-shaped masses of tissue in the lung region. Early diagnosis and treatment of lung nodules can significantly improve the quality of patients' lives. Because of their small size and the interlaced nature of chest ...
Lung Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted Humans Machine Learning Sensitivity and Specificity False Positive Reactions Radiography, Thoracic
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Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification.

IEEE transactions on bio-medical engineering Sep 1, 2016
OBJECTIVE: We present a novel variant of the bag-of-visual-words (BoVW) method for automated medical image classification.
Sensitivity and Specificity Radiographic Image Interpretation, Computer-Assisted Pattern Recognition, Automated Radiography, Thoracic Subtraction Technique Dictionaries as Topic Machine Learning Tomography, X-Ray Computed Reproducibility of Results Humans
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Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain.

Medical image analysis Aug 16, 2016
Suppression of bony structures in chest radiographs (CXRs) is potentially useful for radiologists and computer-aided diagnostic schemes. In this paper, we present an effective deep learning method for bone suppression in single conventional CXR using...
Bone and Bones Neural Networks, Computer Algorithms Diagnosis, Computer-Assisted Radiography, Thoracic
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Fuzzy Clustering Applied to ROI Detection in Helical Thoracic CT Scans with a New Proposal and Variants.

BioMed research international Jul 18, 2016
The detection of pulmonary nodules is one of the most studied problems in the field of medical image analysis due to the great difficulty in the early detection of such nodules and their social impact. The traditional approach involves the developmen...
Radiography, Thoracic Tomography, Spiral Computed Humans Reproducibility of Results Algorithms Fuzzy Logic Sensitivity and Specificity Solitary Pulmonary Nodule Lung Neoplasms Pattern Recognition, Automated
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Multiple Kernel Point Set Registration.

IEEE transactions on medical imaging Dec 22, 2015
The finite Gaussian mixture model with kernel correlation is a flexible tool that has recently received attention for point set registration. While there are many algorithms for point set registration presented in the literature, an important issue a...
Models, Theoretical Humans Radiography, Thoracic Machine Learning Normal Distribution Four-Dimensional Computed Tomography Pulmonary Disease, Chronic Obstructive Algorithms Animals Databases, Factual Image Processing, Computer-Assisted
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Locality-constrained Subcluster Representation Ensemble for lung image classification.

Medical image analysis Mar 24, 2015
In this paper, we propose a new Locality-constrained Subcluster Representation Ensemble (LSRE) model, to classify high-resolution computed tomography (HRCT) images of interstitial lung diseases (ILDs). Medical images normally exhibit large intra-clas...
Sensitivity and Specificity Lung Radiographic Image Interpretation, Computer-Assisted Humans Radiographic Image Enhancement Lung Diseases, Interstitial Machine Learning Pattern Recognition, Automated Algorithms Radiography, Thoracic Tomography, X-Ray Computed
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