AIMC Topic: Diagnostic Imaging

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Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio.

Acta biomaterialia
Elasticity imaging is a technique that discovers the spatial distribution of mechanical properties of tissue using deformation and force measurements under various loading conditions. Given the complexity of this discovery, most existing methods appr...

Mapping the Landscape of Care Providers' Quality Assurance Approaches for AI in Diagnostic Imaging.

Journal of digital imaging
The discussion on artificial intelligence (AI) solutions in diagnostic imaging has matured in recent years. The potential value of AI adoption is well established, as are the potential risks associated. Much focus has, rightfully, been on regulatory ...

Artificial intelligence and automation in endoscopy and surgery.

Nature reviews. Gastroenterology & hepatology
Modern endoscopy relies on digital technology, from high-resolution imaging sensors and displays to electronics connecting configurable illumination and actuation systems for robotic articulation. In addition to enabling more effective diagnostic and...

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends.

IEEE transactions on pattern analysis and machine intelligence
High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imag...

Deep label fusion: A generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation.

Medical image analysis
Deep convolutional neural networks (DCNN) achieve very high accuracy in segmenting various anatomical structures in medical images but often suffer from relatively poor generalizability. Multi-atlas segmentation (MAS), while less accurate than DCNN i...

Trends in clinical validation and usage of US Food and Drug Administration-cleared artificial intelligence algorithms for medical imaging.

Clinical radiology
AIM: To examine the current landscape of US Food and Drug Administration (FDA)-approved artificial intelligence (AI) medical imaging devices and identify trends in clinical validation strategy.

Artificial intelligence-based methods for fusion of electronic health records and imaging data.

Scientific reports
Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalize...

The transformational potential of molecular radiomics.

Journal of medical radiation sciences
Conventional radiomics in nuclear medicine involve hand-crafted and computer-assisted regions of interest. Recent developments in artificial intelligence (AI) have seen the emergence of AI-augmented segmentation and extraction of lower order traditio...