AI Medical Compendium Topic

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X-Rays

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Automated Registration for Dual-View X-Ray Mammography Using Convolutional Neural Networks.

IEEE transactions on bio-medical engineering
OBJECTIVE: Automated registration algorithms for a pair of 2D X-ray mammographic images taken from two standard imaging angles, namely, the craniocaudal (CC) and the mediolateral oblique (MLO) views, are developed.

Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning.

BMC medical imaging
BACKGROUND: Nowadays doctors and radiologists are overwhelmed with a huge amount of work. This led to the effort to design different Computer-Aided Diagnosis systems (CAD system), with the aim of accomplishing a faster and more accurate diagnosis. Th...

Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review.

Journal of medical systems
There has been an explosive growth in research over the last decade exploring machine learning techniques for analyzing chest X-ray (CXR) images for screening cardiopulmonary abnormalities. In particular, we have observed a strong interest in screeni...

LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images.

Computers in biology and medicine
According to the World Health Organization, an estimate of more than five million infections and 355,000 deaths have been recorded worldwide since the emergence of the coronavirus disease (COVID-19). Various researchers have developed interesting and...

Deep learning of longitudinal chest X-ray and clinical variables predicts duration on ventilator and mortality in COVID-19 patients.

Biomedical engineering online
OBJECTIVES: To use deep learning of serial portable chest X-ray (pCXR) and clinical variables to predict mortality and duration on invasive mechanical ventilation (IMV) for Coronavirus disease 2019 (COVID-19) patients.

RadioBERT: A deep learning-based system for medical report generation from chest X-ray images using contextual embeddings.

Journal of biomedical informatics
BACKGROUND: Increasing number of chest X-ray (CXR) examinations in radiodiagnosis departments burdens radiologists' and makes the timely generation of accurate radiological reports highly challenging. An automatic radiological report generation (ARRG...

Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement.

PloS one
In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs f...

The effect of an artificial intelligence algorithm on chest X-ray interpretation of radiology residents.

The British journal of radiology
OBJECTIVE: Chest X-rays are the most commonly performed diagnostic examinations. An artificial intelligence (AI) system that evaluates the images fast and accurately help reducing workflow and management of the patients. An automated assistant may re...

GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-Ray Images.

IEEE journal of biomedical and health informatics
Locating diseases in chest X-ray images with few careful annotations saves large human effort. Recent works approached this task with innovative weakly-supervised algorithms such as multi-instance learning (MIL) and class activation maps (CAM), howev...

AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model.

Computational intelligence and neuroscience
Tuberculosis (TB) is an airborne disease caused by . It is imperative to detect cases of TB as early as possible because if left untreated, there is a 70% chance of a patient dying within 10 years. The necessity for supplementary tools has increased ...