AIMC Topic: Radiography
Clear Filters Showing 691 to 700 of 1088 articles
Deep transfer learning can be used for the detection of hip joints in pelvis radiographs and the classification of their hip dysplasia status.
Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Apr 5, 2021
Reports of machine learning implementations in veterinary imaging are infrequent but changes in machine learning architecture and access to increased computing power will likely prompt increased interest. This diagnostic accuracy study describes a pa...
Editorial for "A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion".
Journal of magnetic resonance imaging : JMRI
Apr 4, 2021
Three-dimensional image volumes from two-dimensional digitally reconstructed radiographs: A deep learning approach in lower limb CT scans.
Medical physics
Apr 3, 2021
PURPOSE: Three-dimensional (3D) reconstructions of the human anatomy have been available for surgery planning or diagnostic purposes for a few years now. The different image modalities usually rely on several consecutive two-dimensional (2D) acquisit...
Contrast-Attentive Thoracic Disease Recognition With Dual-Weighting Graph Reasoning.
IEEE transactions on medical imaging
Apr 1, 2021
Automatic thoracic disease diagnosis is a rising research topic in the medical imaging community, with many potential applications. However, the inconsistent appearances and high complexities of various lesions in chest X-rays currently hinder the de...
Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training.
European radiology
Mar 27, 2021
OBJECTIVES: Diagnostic accuracy of artificial intelligence (AI) pneumothorax (PTX) detection in chest radiographs (CXR) is limited by the noisy annotation quality of public training data and confounding thoracic tubes (TT). We hypothesize that in-ima...
Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development.
Scientific data
Mar 25, 2021
We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset contains the f...
Automating chest radiograph imaging quality control.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Mar 23, 2021
PURPOSE: To automate diagnostic chest radiograph imaging quality control (lung inclusion at all four edges, patient rotation, and correct inspiration) using convolutional neural network models.
Analysis of Potential for User Errors in Mobile Deployment of Radiology Deep Learning for Cardiac Rhythm Device Detection.
Journal of digital imaging
Mar 19, 2021
We examine how convolutional neural networks (CNNs) for cardiac rhythm device detection can exhibit failures in performance under suboptimal deployment scenarios and examine how medically adversarial image presentation can further impair neural netwo...
Image-based deep learning in diagnosing the etiology of pneumonia on pediatric chest X-rays.
Pediatric pulmonology
Mar 19, 2021
PURPOSE: Comparing the efficacy of a deep-learning model in classifying the etiology of pneumonia on pediatric chest X-rays (CXRs) with that of human readers.
Using deep learning and natural language processing models to detect child physical abuse.
Journal of pediatric surgery
Mar 18, 2021
BACKGROUND: The recognition of child physical abuse can be challenging and often requires a multidisciplinary assessment. Deep learning models, based on clinical characteristics, laboratory studies, and imaging findings, were developed to facilitate ...
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