AIMC Topic: Thorax
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Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective.
Pediatric radiology
Sep 1, 2021
Artificial intelligence (AI) applications for chest radiography and chest CT are among the most developed applications in radiology. More than 40 certified AI products are available for chest radiography or chest CT. These AI products cover a wide ra...
Contralaterally Enhanced Networks for Thoracic Disease Detection.
IEEE transactions on medical imaging
Aug 31, 2021
Identifying and locating diseases in chest X-rays are very challenging, due to the low visual contrast between normal and abnormal regions, and distortions caused by other overlapping tissues. An interesting phenomenon is that there exist many simila...
Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning.
Scientific reports
Aug 19, 2021
We examined the feasibility of explainable computer-aided detection of cardiomegaly in routine clinical practice using segmentation-based methods. Overall, 793 retrospectively acquired posterior-anterior (PA) chest X-ray images (CXRs) of 793 patients...
RCoNet: Deformable Mutual Information Maximization and High-Order Uncertainty-Aware Learning for Robust COVID-19 Detection.
IEEE transactions on neural networks and learning systems
Aug 3, 2021
The novel 2019 Coronavirus (COVID-19) infection has spread worldwide and is currently a major healthcare challenge around the world. Chest computed tomography (CT) and X-ray images have been well recognized to be two effective techniques for clinical...
Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph.
Scientific reports
Jul 9, 2021
Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory ...
4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection.
IEEE transactions on neural networks and learning systems
Jul 6, 2021
Due to the high availability of large-scale annotated image datasets, knowledge transfer from pretrained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with da...
A stacked ensemble for the detection of COVID-19 with high recall and accuracy.
Computers in biology and medicine
Jun 30, 2021
The main challenges for the automatic detection of the coronavirus disease (COVID-19) from computed tomography (CT) scans of an individual are: a lack of large datasets, ambiguity in the characteristics of COVID-19 and the detection techniques having...
Chest radiographs and machine learning - Past, present and future.
Journal of medical imaging and radiation oncology
Jun 25, 2021
Despite its simple acquisition technique, the chest X-ray remains the most common first-line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the effi...
Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process.
Scientific reports
Jun 22, 2021
The purpose of this study was to evaluate the diagnostic performance achieved by using fully-connected small artificial neural networks (ANNs) and a simple training process, the Kim-Monte Carlo algorithm, to detect the location of pneumothorax in che...
Performance evaluation of a deep learning image reconstruction (DLIR) algorithm in "double low" chest CTA in children: a feasibility study.
La Radiologia medica
Jun 16, 2021
BACKGROUND: Chest CT angiography (CTA) is a convenient clinical examination for children with an increasing need to reduce both radiation and contrast medium doses. Iterative Reconstruction algorithms are often used to reduce image noise but encounte...
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