The computational needs for AI processing of sensor streams at the edge are increasingly demanding. Edge devices must keep up with high rates of incoming data streams, processing, displaying, archiving, and streaming results or closing a control loop in real time. This requires powerful, efficient, and accurate hardware and software solutions capable of high performance computing.
]]>Whole slide imaging (WSI), the digitization of tissue on slides using whole slide scanners, is gaining traction in healthcare. WSI enables clinicians in histopathology, immunohistochemistry, and cytology to: This post explains how GPU-accelerated toolkits improve the input/output (I/O) performance and image processing tasks. More specifically, it details how to: Time savings…
]]>This is an updated version of Deploying Healthcare AI Workflows with the NVIDIA Clara Deploy Application Framework. The new version adds information about configuring the DICOM adapter and three new reference pipelines. The adoption of AI in hospitals is accelerating rapidly. There are many reasons for this. With Moore’s law broken and computational capability ever increasing…
]]>This post has been updated at Deploying Healthcare AI Workflows with the NVIDIA Clara Deploy Application Framework (updated). The new version adds information about configuring the DICOM adapter and three new reference pipelines. The adoption of AI in hospitals is accelerating rapidly. There are many reasons for this. With Moore’s law broken and computational capability ever…
]]>The medical imaging industry is undergoing a dramatic transformation driven by two technology trends. Artificial Intelligence and software-defined solutions are redefining the medical imaging workflow. Deep learning research in medical imaging is booming. However, most of this research today is performed in isolation and with limited datasets. This leads to overly simplified models which only…
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