AIMC Topic: Radiography

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Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging.

IEEE journal of biomedical and health informatics Apr 14, 2022
A key challenge in training neural networks for a given medical imaging task is the difficulty of obtaining a sufficient number of manually labeled examples. In contrast, textual imaging reports are often readily available in medical records and cont...
Neural Networks, Computer Radiography Diagnostic Imaging Humans
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Automatic diagnosis and grading of patellofemoral osteoarthritis from the axial radiographic view: a deep learning-based approach.

Acta radiologica (Stockholm, Sweden : 1987) Apr 12, 2022
BACKGROUND: Patellofemoral osteoarthritis (PFOA) has a high prevalence and is assessed on axial radiography of the patellofemoral joint (PFJ). A deep learning (DL)-based approach could help radiologists automatically diagnose and grade PFOA via inter...
Osteoarthritis, Knee Humans Retrospective Studies Predictive Value of Tests Radiography Deep Learning
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COV-DLS: Prediction of COVID-19 from X-Rays Using Enhanced Deep Transfer Learning Techniques.

Journal of healthcare engineering Apr 11, 2022
In this paper, modifications in neoteric architectures such as VGG16, VGG19, ResNet50, and InceptionV3 are proposed for the classification of COVID-19 using chest X-rays. The proposed architectures termed "COV-DLS" consist of two phases: heading mode...
Radiography X-Rays Humans COVID-19 SARS-CoV-2 Deep Learning
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Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors.

European radiology Apr 9, 2022
OBJECTIVES: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists.
Bone Neoplasms Radiography Middle Aged Adolescent X-Rays Tomography, X-Ray Computed Female Young Adult Adult Machine Learning Retrospective Studies Humans
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FM-Net: Deep Learning Network for the Fundamental Matrix Estimation from Biplanar Radiographs.

Computer methods and programs in biomedicine Apr 6, 2022
BACKGROUND AND OBJECTIVE: The fundamental matrix estimation is a classic problem in computer vision. The traditional algorithms require high-precision correspondences. However, correspondences in biplanar radiographs are difficult to match accurately...
Image Processing, Computer-Assisted Algorithms Radiography Deep Learning
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Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model.

Nature communications Apr 6, 2022
The inability to accurately, efficiently label large, open-access medical imaging datasets limits the widespread implementation of artificial intelligence models in healthcare. There have been few attempts, however, to automate the annotation of such...
Artificial Intelligence Delivery of Health Care Radiography Humans Thorax X-Rays
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Automatic prosthetic-parameter estimation from anteroposterior pelvic radiographs after total hip arthroplasty using deep learning-based keypoint detection.

The international journal of medical robotics + computer assisted surgery : MRCAS Apr 4, 2022
BACKGROUND: X-ray is a necessary tool for post-total hip arthroplasty (THA) check-ups; however, parameter measurements are time-consuming. We proposed a deep learning tool, BKNet that automates localization of landmarks with parameter measurements.
Radiography Observer Variation Tomography, X-Ray Computed Deep Learning Humans Arthroplasty, Replacement, Hip
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Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning.

Scientific reports Apr 4, 2022
Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from ...
Deep Learning ROC Curve Humans Radiography COVID-19
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Artificial intelligence-based detection of atrial fibrillation from chest radiographs.

European radiology Mar 31, 2022
OBJECTIVE: The purpose of this study was to develop an artificial intelligence (AI)-based model to detect features of atrial fibrillation (AF) on chest radiographs.
Radiography, Thoracic Aged Aged, 80 and over Retrospective Studies Female Deep Learning Male Radiography Humans Middle Aged Atrial Fibrillation Artificial Intelligence
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H-SegNet: hybrid segmentation network for lung segmentation in chest radiographs using mask region-based convolutional neural network and adaptive closed polyline searching method.

Physics in medicine and biology Mar 29, 2022
Chest x-ray (CXR) is one of the most commonly used imaging techniques for the detection and diagnosis of pulmonary diseases. One critical component in many computer-aided systems, for either detection or diagnosis in digital CXR, is the accurate segm...
Image Processing, Computer-Assisted Radiography Neural Networks, Computer Lung Diseases Lung Humans Thorax
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