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

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Novel artificial neural network and linear regression based equation for estimating visceral adipose tissue volume.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND: There is a growing interest in fast and reliable assessment of abdominal visceral adipose tissue (VAT) volume for risk stratification of metabolic disorders. However, imaging based measurement of VAT is costly and limited by scanner avail...

Limited angle tomography for transmission X-ray microscopy using deep learning.

Journal of synchrotron radiation
In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision with other system parts or high attenuation at certain tilting angles. Image reconstruction from such l...

External validation and transfer learning of convolutional neural networks for computed tomography dental artifact classification.

Physics in medicine and biology
Quality assurance of data prior to use in automated pipelines and image analysis would assist in safeguarding against biases and incorrect interpretation of results. Automation of quality assurance steps would further improve robustness and efficienc...

Learning metal artifact reduction in cardiac CT images with moving pacemakers.

Medical image analysis
Metal objects in the human heart such as implanted pacemakers frequently lead to heavy artifacts in reconstructed CT image volumes. Due to cardiac motion, common metal artifact reduction methods which assume a static object during CT acquisition are ...

Liver lesion localisation and classification with convolutional neural networks: a comparison between conventional and spectral computed tomography.

Biomedical physics & engineering express
PURPOSE: To evaluate the benefit of the additional available information present in spectral CT datasets, as compared to conventional CT datasets, when utilizing convolutional neural networks for fully automatic localisation and classification of liv...

DBT Masses Automatic Segmentation Using U-Net Neural Networks.

Computational and mathematical methods in medicine
To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture. Firstly, to suppress the background tissue noise and e...

Multicenter Computer-Aided Diagnosis for Lymph Nodes Using Unsupervised Domain-Adaptation Networks Based on Cross-Domain Confounding Representations.

Computational and mathematical methods in medicine
To achieve the robust high-performance computer-aided diagnosis systems for lymph nodes, CT images may be typically collected from multicenter data, which cause the isolated performance of the model based on different data source centers. The variabi...