AIMC Topic: X-Rays

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Attention UW-Net: A fully connected model for automatic segmentation and annotation of chest X-ray.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Automatic segmentation and annotation of medical image plays a critical role in scientific research and the medical care community. Automatic segmentation and annotation not only increase the efficiency of clinical workflow,...

Enhanced detection of threat materials by dark-field x-ray imaging combined with deep neural networks.

Nature communications
X-ray imaging has been boosted by the introduction of phase-based methods. Detail visibility is enhanced in phase contrast images, and dark-field images are sensitive to inhomogeneities on a length scale below the system's spatial resolution. Here we...

Automatic Detection of Cases of COVID-19 Pneumonia from Chest X-ray Images and Deep Learning Approaches.

Computational intelligence and neuroscience
Machine learning has already been used as a resource for disease detection and health care as a complementary tool to help with various daily health challenges. The advancement of deep learning techniques and a large amount of data-enabled algorithms...

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention.

Computers in biology and medicine
Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA) network is proposed via chest X-ray (CXR) image classification. First, to overcome the data shortage and improve the robustness of our network, a pixel-level image...

Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data.

Scientific reports
With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data contains ...

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization.

Frontiers in public health
Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to redu...

CheXGAT: A disease correlation-aware network for thorax disease diagnosis from chest X-ray images.

Artificial intelligence in medicine
Chest X-ray (CXR) imaging is one of the most common diagnostic imaging techniques in clinical diagnosis and is usually used for radiological examinations to screen for thorax diseases. In this paper, we propose a novel computer-aided diagnosis (CAD) ...

Development of a computer-aided quality assurance support system for identifying hand X-ray image direction using deep convolutional neural network.

Radiological physics and technology
The convenience of imaging has improved with digitization; however, there has been no progress in the methods used to prevent human error. Therefore, radiographic incidents and accidents are not prevented. In Japan, image interpretation is conducted ...

Deep learning-based dental implant recognition using synthetic X-ray images.

Medical & biological engineering & computing
A novel algorithm for generating artificial training samples from triangulated three-dimensional (3D) surface models within the context of dental implant recognition is proposed. The proposed algorithm is based on the calculation of two-dimensional (...

Development of deep learning chest X-ray model for cardiac dose prediction in left-sided breast cancer radiotherapy.

Scientific reports
Deep inspiration breath-hold (DIBH) is widely used to reduce the cardiac dose in left-sided breast cancer radiotherapy. This study aimed to develop a deep learning chest X-ray model for cardiac dose prediction to select patients with a potentially hi...