AIMC Topic: Solitary Pulmonary Nodule

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Towards automatic pulmonary nodule management in lung cancer screening with deep learning.

Scientific reports Apr 19, 2017
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines...
Deep Learning Lung Neoplasms Solitary Pulmonary Nodule Early Detection of Cancer Humans Tomography, X-Ray Computed
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A ℓ norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD.

Computer methods and programs in biomedicine Dec 15, 2016
OBJECTIVE: The aim of this paper is to describe a novel algorithm for False Positive Reduction in lung nodule Computer Aided Detection(CAD).
Machine Learning False Positive Reactions Diagnosis, Computer-Assisted Humans Support Vector Machine Solitary Pulmonary Nodule
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Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM.

Medical & biological engineering & computing Oct 3, 2016
Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LID...
Sensitivity and Specificity Male Middle Aged Pattern Recognition, Automated Models, Genetic Algorithms Humans Radiographic Image Enhancement Support Vector Machine Tomography, X-Ray Computed Solitary Pulmonary Nodule Female Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results
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Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection.

IEEE transactions on bio-medical engineering Sep 26, 2016
OBJECTIVE: False positive reduction is one of the most crucial components in an automated pulmonary nodule detection system, which plays an important role in lung cancer diagnosis and early treatment. The objective of this paper is to effectively add...
Humans Neural Networks, Computer Machine Learning Algorithms Diagnostic Errors Imaging, Three-Dimensional Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Reproducibility of Results Sensitivity and Specificity Pattern Recognition, Automated False Positive Reactions
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Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets.

BioMed research international Sep 18, 2016
In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detec...
Pattern Recognition, Automated Radiographic Image Enhancement Subtraction Technique Sensitivity and Specificity Humans Lung Neoplasms Tomography, X-Ray Computed Support Vector Machine Reproducibility of Results Solitary Pulmonary Nodule
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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...
Pattern Recognition, Automated Radiography, Thoracic Tomography, Spiral Computed Lung Neoplasms Fuzzy Logic Solitary Pulmonary Nodule Humans Algorithms Reproducibility of Results Sensitivity and Specificity
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Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans.

Scientific reports Apr 15, 2016
This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results...
Solitary Pulmonary Nodule Breast Datasets as Topic Machine Learning Algorithms Tomography, X-Ray Computed Female Reproducibility of Results Diagnosis, Computer-Assisted Ultrasonography Humans
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Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box.

Medical image analysis Sep 8, 2015
In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. ...
Imaging, Three-Dimensional Lung Neoplasms Solitary Pulmonary Nodule Algorithms Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Humans Pattern Recognition, Automated Reproducibility of Results Neural Networks, Computer Radiographic Image Enhancement Sensitivity and Specificity Software Subtraction Technique
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Computer-aided lung nodule recognition by SVM classifier based on combination of random undersampling and SMOTE.

Computational and mathematical methods in medicine Apr 6, 2015
In lung cancer computer-aided detection/diagnosis (CAD) systems, classification of regions of interest (ROI) is often used to detect/diagnose lung nodule accurately. However, problems of unbalanced datasets often have detrimental effects on the perfo...
Reproducibility of Results Support Vector Machine Models, Statistical Humans Diagnosis, Computer-Assisted Cluster Analysis Radiographic Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Algorithms Solitary Pulmonary Nodule Pattern Recognition, Automated Tomography, X-Ray Computed Lung Neoplasms
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Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset.

Medical image analysis Feb 23, 2015
We present new pulmonary nodule segmentation algorithms for computed tomography (CT). These include a fully-automated (FA) system, a semi-automated (SA) system, and a hybrid system. Like most traditional systems, the new FA system requires only a sin...
Tomography, X-Ray Computed Reproducibility of Results Sensitivity and Specificity Lung Neoplasms Neural Networks, Computer Algorithms Databases, Factual Radiographic Image Interpretation, Computer-Assisted Pattern Recognition, Automated Radiography, Thoracic Radiographic Image Enhancement Solitary Pulmonary Nodule Regression Analysis Data Interpretation, Statistical Humans
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