AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Automated Histological Segmentation on Micro Computed Tomography Images of Atherosclerotic Arteries.
European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery
Feb 13, 2021
Machine Learning-Derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins From Cardiac Computed Tomography Scans Are Associated With Risk of Recurrence of Atrial Fibrillation Postablation.
Circulation. Arrhythmia and electrophysiology
Feb 12, 2021
[Figure: see text].
Treatment Outcome
Action Potentials
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted
Risk Factors
Male
Middle Aged
Risk Assessment
Recurrence
Atrial Function, Left
Fractals
Pulmonary Veins
Atrial Fibrillation
Heart Atria
Heart Rate
Catheter Ablation
Humans
Machine Learning
Retrospective Studies
Tomography, X-Ray Computed
Female
Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning-based CT Section Thickness Reduction.
Radiology
Feb 9, 2021
Background Studies on the optimal CT section thickness for detecting subsolid nodules (SSNs) with computer-aided detection (CAD) are lacking. Purpose To assess the effect of CT section thickness on CAD performance in the detection of SSNs and to inve...
Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction.
European radiology
Feb 8, 2021
OBJECTIVE: To evaluate the effect of a commercial deep learning algorithm on the image quality of chest CT, focusing on the upper abdomen.
Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy.
European radiology experimental
Feb 2, 2021
BACKGROUND: We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy.
Discriminative Feature Learning for Thorax Disease Classification in Chest X-ray Images.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Feb 1, 2021
This paper focuses on the thorax disease classification problem in chest X-ray (CXR) images. Different from the generic image classification task, a robust and stable CXR image analysis system should consider the unique characteristics of CXR images....
Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study.
Cardiovascular diabetology
Jan 29, 2021
BACKGROUND: We sought to evaluate the association of metabolic syndrome (MetS) and computed tomography (CT)-derived cardiometabolic biomarkers (non-alcoholic fatty liver disease [NAFLD] and epicardial adipose tissue [EAT] measures) with long-term ris...
Deep Learning
Time Factors
Metabolic Syndrome
Adipose Tissue
Male
Middle Aged
Risk Assessment
Prevalence
Humans
Los Angeles
Prospective Studies
Predictive Value of Tests
Tomography, X-Ray Computed
Heart Diseases
Prognosis
Radiographic Image Interpretation, Computer-Assisted
Adiposity
Female
Registries
Pericardium
Aged
Non-alcoholic Fatty Liver Disease
Cardiometabolic Risk Factors
Aged, 80 and over
Extracting and Learning Fine-Grained Labels from Chest Radiographs.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today. Recently, a number of researchers have begun working on large chest X-ray datasets to develop deep learning models for recognition of a handful o...
Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction.
Japanese journal of radiology
Jan 15, 2021
PURPOSE: To evaluate the usefulness of the deep learning image reconstruction (DLIR) to enhance the image quality of abdominal CT, compared to iterative reconstruction technique.
The Feature Ambiguity Mitigate Operator model helps improve bone fracture detection on X-ray radiograph.
Scientific reports
Jan 15, 2021
This study was performed to propose a method, the Feature Ambiguity Mitigate Operator (FAMO) model, to mitigate feature ambiguity in bone fracture detection on radiographs of various body parts. A total of 9040 radiographic studies were extracted. Th...
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