AI Medical Compendium Topic

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Thorax

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Hybrid Neural Networks for Mortality Prediction from LDCT Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Known for its high morbidity and mortality rates, lung cancer poses a significant threat to human health and well-being. However, the same population is also at high risk for other deadly diseases, such as cardiovascular disease. Since Low-Dose CT (L...

Number and Angle Analysis in UWB Radar Deployment for Vital Sign Monitoring.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In recent years, more studies focus on the UltraWide Band (UWB) radar to provide a noncontact vital sign monitoring service. To further improve the accuracy of vital sign monitoring, the UWB radar network composed by multiple radars is considered for...

Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging.

Journal of thoracic imaging
Advances in technology have always had the potential and opportunity to shape the practice of medicine, and in no medical specialty has technology been more rapidly embraced and adopted than radiology. Machine learning and deep neural networks promis...

[The future of computer-aided diagnostics in chest computed tomography].

Khirurgiia
Recently, more and more attention has been paid to the utility of artificial intelligence in medicine. Radiology differs from other medical specialties with its high digitalization, so most software developers operationalize this area of medicine. Th...

Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automated segmentation of the spleen in CT volumes is difficult due to variations in size, shape, and position of the spleen within the abdominal cavity as well as similarity of intensity values among organs in the abdominal cavity. In this paper we ...

Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration.

Medical physics
PURPOSE: In an attempt to overcome several hurdles that exist in organ segmentation approaches, the authors previously described a general automatic anatomy recognition (AAR) methodology for segmenting all major organs in multiple body regions body-w...

Medical image segmentation via atlases and fuzzy object models: Improving efficacy through optimum object search and fewer models.

Medical physics
PURPOSE: Statistical object shape models (SOSMs), known as probabilistic atlases, are popular in medical image segmentation. They register an image into the atlas coordinate system, such that a desired object can be delineated from the constraints of...

A comparative study for chest radiograph image retrieval using binary texture and deep learning classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In this work various approaches are investigated for X-ray image retrieval and specifically chest pathology retrieval. Given a query image taken from a data set of 443 images, the objective is to rank images according to similarity. Different feature...

Automatic learning-based beam angle selection for thoracic IMRT.

Medical physics
PURPOSE: The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues. Int...