AI Medical Compendium Topic:
Radiographic Image Interpretation, Computer-Assisted

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Artificial intelligence and deep learning - Radiology's next frontier?

Clinical imaging
Tracing the use of computers in the radiology department from administrative functions through image acquisition, storage, and reporting, to early attempts at improved diagnosis, we begin to imagine possible new frontiers for their use in exam interp...

Low-Dose Lung CT Image Restoration Using Adaptive Prior Features From Full-Dose Training Database.

IEEE transactions on medical imaging
The valuable structure features in full-dose computed tomography (FdCT) scans can be exploited as prior knowledge for low-dose CT (LdCT) imaging. However, lacking the capability to represent local characteristics of interested structures of the LdCT ...

Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network.

IEEE transactions on medical imaging
Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction algorithms are one of the most promising way to compensate for the increased noise due to reduction of photon flu...

Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning.

IEEE transactions on medical imaging
We describe an automated methodology for the analysis of unregistered cranio-caudal (CC) and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk of developing breast cancer. The main innovation behind this methodolog...

Please Don't Move-Evaluating Motion Artifact From Peripheral Quantitative Computed Tomography Scans Using Textural Features.

Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry
Most imaging methods, including peripheral quantitative computed tomography (pQCT), are susceptible to motion artifacts particularly in fidgety pediatric populations. Methods currently used to address motion artifact include manual screening (visual ...

An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network.

IEEE journal of biomedical and health informatics
High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around t...

Fully automatic detection of lung nodules in CT images using a hybrid feature set.

Medical physics
PURPOSE: The aim of this study was to develop a novel technique for lung nodule detection using an optimized feature set. This feature set has been achieved after rigorous experimentation, which has helped in reducing the false positives significantl...