Virtualized computing platform for inferencing, advanced processing, and machine learning applications

Abstract

In various examples, a virtualized computing platform for advanced computing operations including image reconstruction, segmentation, processing, analysis, visualization, and deep learning may be provided. The platform may allow for inference pipeline customization by selecting, organizing, and adapting constructs of task containers for local, on-premises implementation. Within the task containers, machine learning models generated off-premises may be leveraged and updated for location specific implementation to perform image processing operations. As a result, and by using the virtualized computing platform, facilities such as hospitals and clinics may more seamlessly train, deploy, and integrate machine learning models within a production environment for providing informative and actionable medical information to practitioners.

Description

Background technique [0001] Throughout recent history, imaging procedures have been an integral part of research and diagnostics in many industries, including healthcare and medical research. Medical imaging equipment—such as computed tomography (CT) scanners, positron emission technology (PET) scanners, nuclear imagers, X-ray machines, ultrasound machines, and magnetic resonance imaging (MRI) machines—is widely used in paramedical professions Researchers and data scientists visualize the anatomy of subjects to identify abnormalities, determine diagnoses, and study organ function and disease. Well-equipped modern hospitals and laboratories can deploy any number of such medical imaging devices in-house. With the advancement of computing technology, especially graphics processing technology, graphics processing units (GPUs) are increasingly used in medical imaging equipment to improve their performance and functionality. [0002] Despite these advances in technology, healthcar...

AI Technical Summary

Problems solved by technology

As a result, as more accurate imaging techniques and equipment are developed, hospitals are limited by the technology of their current medical imaging equipment, in addition to more optimized and informative machine learning models, reducing the ability of hospitals to provide the most accurate and accurate The ability of imaging techniques to benefit diagnosis and treatment of patients

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