AIMC Topic: Tomography, X-Ray Computed

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Deep learning and medical diagnosis.

Lancet (London, England) Nov 9, 2019
Algorithms Retrospective Studies Tomography, X-Ray Computed Deep Learning Machine Learning
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Deep convolutional neural network for reduction of contrast-enhanced region on CT images.

Journal of radiation research Oct 23, 2019
This study aims to produce non-contrast computed tomography (CT) images using a deep convolutional neural network (CNN) for imaging. Twenty-nine patients were selected. CT images were acquired without and with a contrast enhancement medium. The trans...
Humans Neural Networks, Computer Tomography, X-Ray Computed Time Factors Contrast Media Dose-Response Relationship, Radiation
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[Application value of Revolution CT combining three-dimensional visualization technique in precision resection of hepatic alveolar echinococcosis].

Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control Oct 14, 2019
OBJECTIVE: To explore the application value of Revolution CT combining three -dimensional visualization technique in the precision resection of hepatic alveolar echinococcosis.
Imaging, Three-Dimensional Hepatectomy Humans Echinococcosis, Hepatic Retrospective Studies Tomography, X-Ray Computed
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Simulated Lumbar Dual-Energy X-Ray Absorptiometry With Machine Learning Algorithms: Why Radiologists Who Interpret Abdominal CTs Should Care.

Journal of the American College of Radiology : JACR Oct 1, 2019
Humans Machine Learning Algorithms Absorptiometry, Photon Tomography, X-Ray Computed Radiologists
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Machine Learning-Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer.

Clinical and translational gastroenterology Oct 1, 2019
INTRODUCTION: Adverse histopathological status (AHS) decreases outcomes of gastric cancer (GC). With the lack of a single factor with great reliability to preoperatively predict AHS, we developed a computational approach by integrating large-scale im...
Neoplasm Staging Neoplasm Recurrence, Local Gastrectomy Kaplan-Meier Estimate Stomach Disease-Free Survival Preoperative Period Follow-Up Studies Contrast Media Neoplasm Invasiveness Middle Aged Prevalence Image Processing, Computer-Assisted Prognosis Retrospective Studies Tomography, X-Ray Computed Stomach Neoplasms Male Female Reproducibility of Results Humans Machine Learning Computer Simulation
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Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer.

Korean journal of radiology Oct 1, 2019
OBJECTIVE: To retrospectively assess the effect of CT slice thickness on the reproducibility of radiomic features (RFs) of lung cancer, and to investigate whether convolutional neural network (CNN)-based super-resolution (SR) algorithms can improve t...
Retrospective Studies Tomography, X-Ray Computed Female Reproducibility of Results Humans Neural Networks, Computer Algorithms Image Processing, Computer-Assisted Lung Neoplasms Radiometry Male Middle Aged Deep Learning
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Computer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images.

Investigative radiology Oct 1, 2019
OBJECTIVES: The objective of this study is to assess the performance of a computer-aided diagnosis (CAD) system (INTACT system) for the automatic classification of high-resolution computed tomography images into 4 radiological diagnostic categories a...
Humans Aged, 80 and over Biopsy Sensitivity and Specificity Pulmonary Fibrosis Male Image Interpretation, Computer-Assisted Diagnosis, Computer-Assisted Deep Learning Middle Aged Lung Female Reproducibility of Results Algorithms Aged Retrospective Studies Tomography, X-Ray Computed
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Application of Deep Learning to Pancreatic Cancer Detection: Lessons Learned From Our Initial Experience.

Journal of the American College of Radiology : JACR Sep 1, 2019
Tomography, X-Ray Computed Female Male Case-Control Studies Humans Workflow Carcinoma, Pancreatic Ductal Quality Improvement Automation Deep Learning Imaging, Three-Dimensional Pancreatic Neoplasms Forecasting Retrospective Studies
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The Role of Generative Adversarial Networks in Radiation Reduction and Artifact Correction in Medical Imaging.

Journal of the American College of Radiology : JACR Sep 1, 2019
Adversarial networks were developed to complete powerful image-processing tasks on the basis of example images provided to train the networks. These networks are relatively new in the field of deep learning and have proved to have unique strengths th...
Magnetic Resonance Imaging Diagnostic Imaging Positron-Emission Tomography Artifacts Radiation Exposure Humans Patient Safety Artificial Intelligence Tomography, X-Ray Computed Forecasting Deep Learning
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[Research progress on computed tomography image detection and classification of pulmonary nodule based on deep learning].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Aug 25, 2019
Computer-aided diagnosis based on computed tomography (CT) image can realize the detection and classification of pulmonary nodules, and improve the survival rate of early lung cancer, which has important clinical significance. In recent years, with t...
Humans Deep Learning Tomography, X-Ray Computed Lung Neoplasms Reproducibility of Results Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted
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