AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Deep Learning for the Classification of Small (≤2 cm) Pulmonary Nodules on CT Imaging: A Preliminary Study.

Academic radiology Nov 25, 2019
RATIONALE AND OBJECTIVES: We aimed to present a deep learning-based malignancy prediction model (CT-lungNET) that is simpler and faster to use in the diagnosis of small (≤2 cm) pulmonary nodules on nonenhanced chest CT and to preliminarily evaluate i...
Humans Tomography, X-Ray Computed Deep Learning Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted
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A manifold learning regularization approach to enhance 3D CT image-based lung nodule classification.

International journal of computer assisted radiology and surgery Nov 25, 2019
PURPOSE: Diagnosis of lung cancer requires radiologists to review every lung nodule in CT images. Such a process can be very time-consuming, and the accuracy is affected by many factors, such as experience of radiologists and available diagnosis time...
Radiographic Image Interpretation, Computer-Assisted Deep Learning Imaging, Three-Dimensional Humans Lung Neoplasms Neural Networks, Computer Solitary Pulmonary Nodule Tomography, X-Ray Computed Sensitivity and Specificity
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The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population.

Skeletal radiology Nov 23, 2019
OBJECTIVE: Osteoporosis is hard to detect before it manifests symptoms and complications. In this study, we evaluated machine learning models for identifying individuals with abnormal bone mineral density (BMD) through an analysis of spine X-ray feat...
Retrospective Studies Osteoporosis Republic of Korea Female Sensitivity and Specificity Male Middle Aged Predictive Value of Tests Humans Machine Learning Bone Density Radiography Radiographic Image Interpretation, Computer-Assisted Spine Absorptiometry, Photon
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Evaluation of a Deep Learning-Based Automated CT Coronary Artery Calcium Scoring Algorithm.

JACC. Cardiovascular imaging Nov 13, 2019
Coronary Vessels Reproducibility of Results Aged Coronary Angiography Vascular Calcification Humans Male Computed Tomography Angiography Radiographic Image Interpretation, Computer-Assisted Software Deep Learning Middle Aged Severity of Illness Index Cardiac-Gated Imaging Techniques Coronary Artery Disease Retrospective Studies Electrocardiography Female Diagnosis, Computer-Assisted Automation Predictive Value of Tests
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Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs.

Radiology Nov 12, 2019
Background Multicenter studies are required to validate the added benefit of using deep convolutional neural network (DCNN) software for detecting malignant pulmonary nodules on chest radiographs. Purpose To compare the performance of radiologists in...
Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Sensitivity and Specificity Male Middle Aged Lung Radiography, Thoracic Humans Neural Networks, Computer Retrospective Studies Female Reproducibility of Results Aged Young Adult Adult
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Lung Nodule Detection based on Ensemble of Hand Crafted and Deep Features.

Journal of medical systems Nov 8, 2019
Lung cancer is considered as a deadliest disease worldwide due to which 1.76 million deaths occurred in the year 2018. Keeping in view its dreadful effect on humans, cancer detection at a premature stage is a more significant requirement to reduce th...
Tomography, X-Ray Computed Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted Humans Machine Learning Algorithms
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Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification.

European radiology Nov 5, 2019
OBJECTIVE: To evaluate the impact of utilizing digital breast tomosynthesis (DBT) or/and full-field digital mammography (FFDM), and different transfer learning strategies on deep convolutional neural network (DCNN)-based mass classification for breas...
Humans Radiographic Image Enhancement Breast Neoplasms Middle Aged Deep Learning Aged Imaging, Three-Dimensional Mammography Aged, 80 and over Neural Networks, Computer Young Adult Diagnosis, Differential Machine Learning Radiographic Image Interpretation, Computer-Assisted Adult Retrospective Studies Breast Female
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A method for the automated classification of benign and malignant masses on digital breast tomosynthesis images using machine learning and radiomic features.

Radiological physics and technology Nov 4, 2019
In digital mammography, which is used for the early detection of breast tumors, oversight may occur due to overlap between normal tissues and lesions. However, since digital breast tomosynthesis can acquire three-dimensional images, tissue overlappin...
Female Reproducibility of Results Breast Neoplasms Diagnosis, Computer-Assisted Humans Machine Learning Algorithms Bayes Theorem Radiographic Image Interpretation, Computer-Assisted Breast Pattern Recognition, Automated False Positive Reactions ROC Curve Image Processing, Computer-Assisted Support Vector Machine Mammography
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Development of a deep learning model to identify hyperdense MCA sign in patients with acute ischemic stroke.

Japanese journal of radiology Oct 31, 2019
PURPOSE: The aim of this study was to develop an interactive deep learning-assisted identification of the hyperdense middle cerebral artery (MCA) sign (HMCAS) on non-contrast computed tomography (CT) among patients with acute ischemic stroke.
ROC Curve Aged Humans Sensitivity and Specificity Radiographic Image Interpretation, Computer-Assisted Middle Cerebral Artery Retrospective Studies Stroke Male Tomography, X-Ray Computed Brain Ischemia Deep Learning Female
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Artificial Intelligence in Radiology: A Call for Thoughtful Application.

Clinical and translational science Oct 30, 2019
Patient Acceptance of Health Care Radiographic Image Interpretation, Computer-Assisted Artificial Intelligence History, 21st Century Software History, 20th Century Radiology History, 19th Century
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