Scientists at Stanford have developed a machine learning program with broad potential for diagnostic applications which analyzes mass spectrometry data profiling metabolites in a patient sample ("metabolomics" data) and predicts infection status.
Researchers at Stanford have developed a distributed digital "black box" audit trail design for connected and automated vehicle data and software assurance.
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.
Researchers at Stanford University, Technical University of Denmark, and Norwegian University of Science and Technology have developed a software suite that can predict long-term performance of reinforced concrete based on multiple, fundamental, physics phenomenon like humidit
Stanford researchers have integrated concrete durability modeling software into building information models (BIM) for better management, repair, and assessment of structural elements like roads, bridges, dams, buildings, etc.
Using a novel convolutional neural network architecture, PlexusNet can be used for histologic image analysis with smaller parameter and training sets than current state-of-the-art models.
Stanford researchers at the Liao and Xing Labs have developed and tested a machine learning algorithm for augmented detection of bladder cancer. Machine learning has the potential to enhance medical decision making in cancer detection and image analysis.
Researchers at Stanford have demonstrated a new type of energy-efficient and ultrathin memory. This low-energy cost memory is based on stacking orders in the atomically thin limit, associated with tiny changes in the position of one atomic layer with respect to another.
Artificial intelligence can be leveraged to evaluate how facial expressions will be perceived by others. A deep learning neural network is used to generate facial vectors for each image of a person.
Stanford researchers have developed deep learning methods which can more precisely localize the position and orientation of a camera in the lung anatomy in real-time.
Stanford engineers at Zhenan Bao's laboratory have designed a compliance sensor which can identify softness (compliance) of touched objects and provide human-like sensation to robots and prosthetics.
Stanford researchers at the Xing Lab have developed a dosimetric features driven- machine learning model for dose volume histograms (DVHs) and dose prediction for volumetric modulated arc therapy (VMAT) planning.