Stanford researchers have created a single diffusion generative model, DiffusionPoser, that can reconstruct human motion in real-time from arbitrary body sensor configurations, with broad application in a variety of motion capture end uses.
Stanford inventors have created an audio-visual system with a radiotransparent screen provides a means for communication and visual distractions during procedures such as radiation therapy and radiation imaging.
Actigraphy, or the non-invasive study of human activity-rest cycles, is a field of study of growing importance as ambulatory and at-home monitoring of patients becomes more popular.
Researchers at Stanford University have established a deep learning segmentation algorithm for non-contrast CT images to aid clinicians in decision making and improve the speed of symptom to treatment in acute ischemic stroke
Stanford inventors have developed TrueImage, a machine learning algorithm to assess the quality of patient images sent in for telemedicine appointments.
Volumetric contrast-enhanced ultrasound is a new approach to collect 3D imaging data of a contrast signal. So far, analysis of 3D contrast ultrasound has relied on averaging a set of voxels embedded in an ROI or a VOI.
This software tool takes clinical notes from veterinary electronic medical records and assigns SNOMED-CT VET extension diagnostic codes based on the content written on the notes.
Stanford researchers have developed a statistical method to map tissue activity distribution and photon attenuation, correcting for attenuation in real time without a transmission scan, using Positron Emission Tomography.
Stanford researchers have patented an automated method for generating articulated human models consisting of both morphological and kinematic model data.
Stanford researchers have patented a data-driven method for building a human shape model that spans variation in both subject shape and pose. The method is based on a representation that incorporates both articulated and non-rigid deformations.
Stanford researchers have developed an Exact Dynamic Collision Checker for Animation and CAD/Robotic Applications that is both infallible and efficient.
Researchers from Stanford University and the Max Planck Institute have patented a new marker-less approach to capturing human performances from multi-view video.