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 developed a new machine learning method for extracting gait parameters, such as cadence, step length, peak knee flexion, and Gait Deviation Index (GDI), from a single video.
Stanford researchers at the Xing Lab have developed a novel method using deep neural networks called "Q2MRI" to simultaneously acquire qualitative MR image and quantitative MRI parametric maps without changing the clinical imaging protocol or elongating MRI scan tim
Prof. Alison Marsden and her colleagues have developed a computational framework that uses personalized anatomical information to identify patients that have a high risk for saphenous vein graft (SVG) failure after coronary artery bypass graft (CABG).
Stanford researchers have developed a method that can leverage state-of-the-art techniques that are not clinically feasible to train a neural network to distinguish contrast agents versus background tissue in a way that is safe, real-time, and can expedite the translation of u
Stanford researchers at the Dahl Lab have developed a method to reduce artifacts in ultrasound image reconstruction using a trained convolutional neural network (CNN).
Stanford researchers at the Pratx Lab have developed a new trajectory reconstruction method for tracking moving sources labeled with positron-emitting radionuclides using PET.
Dr. Shreyas Vasanawala and collaborators have developed a nonrigid motion correction technique that will allow for motion-free Magnetic Resonance (MR) images to be obtained even during lengthy scans. Motion is a major source of image artifacts for MR studies.
Stanford researchers have developed a versatile computational approach for easily visualizing and analyzing multidimensional molecular data, such as flow cytometry data.