AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms.
Computational and mathematical methods in medicine
Oct 28, 2020
The American Cancer Society expected to diagnose 276,480 new cases of invasive breast cancer in the USA and 48,530 new cases of noninvasive breast cancer among women in 2020. Early detection of breast cancer, followed by appropriate treatment, can re...
Traditional and New Methods of Bone Age Assessment-An Overview.
Journal of clinical research in pediatric endocrinology
Oct 26, 2020
Bone age is one of biological indicators of maturity used in clinical practice and it is a very important parameter of a child’s assessment, especially in paediatric endocrinology. The most widely used method of bone age assessment is by performing a...
Radiographic Image Interpretation, Computer-Assisted
Observer Variation
Automation
Child Development
Wrist Joint
Reproducibility of Results
Age Determination by Skeleton
Young Adult
Adolescent Development
Humans
Male
Hand Bones
Neural Networks, Computer
Infant
Child, Preschool
Diagnosis, Computer-Assisted
Female
Child
Deep Learning
Infant, Newborn
Age Factors
Adolescent
Predictive Value of Tests
Elastic Registration-driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT.
Radiology
Oct 20, 2020
Background Longitudinal follow-up of interstitial lung diseases (ILDs) at CT mainly relies on the evaluation of the extent of ILD, without accounting for lung shrinkage. Purpose To develop a deep learning-based method to depict worsening of ILD based...
Lesion-aware convolutional neural network for chest radiograph classification.
Clinical radiology
Oct 16, 2020
AIM: To investigate the performance of a deep-learning approach termed lesion-aware convolutional neural network (LACNN) to identify 14 different thoracic diseases on chest X-rays (CXRs).
CT iterative vs deep learning reconstruction: comparison of noise and sharpness.
European radiology
Oct 15, 2020
OBJECTIVES: To compare image noise and sharpness of vessels, liver, and muscle in lower extremity CT angiography between "adaptive statistical iterative reconstruction-V" (ASIR-V) and deep learning reconstruction "TrueFidelity" (TFI).
AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia.
Medical image analysis
Oct 15, 2020
Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme import...
Reproducibility of abnormality detection on chest radiographs using convolutional neural network in paired radiographs obtained within a short-term interval.
Scientific reports
Oct 15, 2020
We evaluated the reproducibility of computer-aided detections (CADs) with a convolutional neural network (CNN) on chest radiographs (CXRs) of abnormal pulmonary patterns in patients, acquired within a short-term interval. Anonymized CXRs (n = 9792) o...
Combination of Deep Learning-Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation.
AJR. American journal of roentgenology
Oct 14, 2020
The objective of our study was to assess the effect of the combination of deep learning-based denoising (DLD) and iterative reconstruction (IR) on image quality and Lung Imaging Reporting and Data System (Lung-RADS) evaluation on chest ultra-low-dos...
Clinical and laboratory data, radiological structured report findings and quantitative evaluation of lung involvement on baseline chest CT in COVID-19 patients to predict prognosis.
La Radiologia medica
Oct 12, 2020
OBJECTIVE: To evaluate by means of regression models the relationships between baseline clinical and laboratory data and lung involvement on baseline chest CT and to quantify the thoracic disease using an artificial intelligence tool and a visual sco...
Deep learning-based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V).
Neuroradiology
Oct 10, 2020
PURPOSE: To compare the image quality of brain computed tomography (CT) images reconstructed with deep learning-based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V).
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