Stanford researchers have designed an AI-based visualization method which can assist project teams to quickly, consistently, and effectively manage change events on any project.
This software is a transformative technology in the fields of AI and digital image processing, offering a breakthrough approach to convolution, particularly for large-scale images.
Stanford researchers have invented a unified AI architecture that integrates foundational models (FMs) with AI techniques for efficient analysis of fMRI data in psychiatric disorders.
Stanford researchers at the Rubin Lab have developed a software system to optimize federated or distributed deep learning methods to overcome current challenges of processing heterogeneous data across institutions such as hospitals.
Stanford researchers have designed a trainable portable device that can rapidly quantify liver steatosis (fat) prior to transplantation without a pathologist. Currently, rapid assessments are hindered by waiting for an available pathologist to provide results.
Stanford researchers at the de la Zerda Lab have developed an innovative alignment methodology using Optical Coherence Tomography (OCT) in conjunction with histopathology to diagnose cancer or determine tumor margins.
Stanford researchers in The Fan Group have developed an optical device that can fine tune the color of each photon in a stream of light. Existing methods simply reroute photons of a particular frequency, but do not actually change the photons frequencies.
As artificial intelligence (AI) algorithms enable transformative new user experiences in mobile computing devices, data security and privacy has become increasingly important.
Researchers in the Murmann Mixed Signal Group have developed a pipelined chip architecture with inverted residual and linear bottlenecks-based networks for energy efficient Machine Learning inference on edge devices.
Stanford researchers have developed an algorithm using deep learning architectures to predict cardiac function (ejection fraction) and trace the endocardium of the left ventricle from ultrasound echocardiogram videos.
In a method of amplifying optical input signals over a wide bandwidth, the optical input signals are applied to an optical waveguide made from a rare-earth-doped amorphous material (e.g., erbium-doped yttrium aluminum oxide material).
Stanford researchers have developed a method for identifying the foveal center in the eye for high resolution retinal mapping in adaptive optics devices using artificial intelligence.
The Murmann lab has developed a method for an extraction information from acoustic signals that utilizes low power consumption. N-path filters are used to decompose the original acoustic signals' waveform before downconverting to lower their Nyquist-rate bandwidth.