Abstract: Artificial reality (XR) experiences today typically only provide users representations of their upper body (e.g., as avatars). Although legs do not have a high range of movement or expression in XR, they are required to bring a sense of believability to digital humans represented in XR. However, tracking legs can be difficult because they are frequently not visible to XR device cameras. Aspects of the present disclosure provide a full body synthesis system that can generate plausible full body poses of users by leveraging generative machine learning, in real time, on an XR device. The full body synthesis system can be flexible to multiple numbers and types of inputs (e.g., positions/rotations/accelerations of joints, computer vision models, etc.), and can generalize users of any height, body scale, and body shape.

Abstract: Methods disclosed herein relate generally to methods for training an algorithm and for using the trained algorithm for detection, segmentation and characterization of object instances in digital images, applicable for detection, segmentation and characterization of tumor burdens in images from brain MRI scans of Glioblastoma patients.

Abstract: Systems and methods disclosed herein relate generally to systems and methods for detection, segmentation and characterization of isolated or overlapping object instances in digital images, applicable for detection, segmentation and characterization of crypts in histological images from patients with gastrointestinal disorders.

Abstract: A computer-implemented method of identifying a tissue type in digital histological images of human or animal tissue comprises training a convolutional neural network CNN to identify a particular target tissue type in a plurality of training data sets of digital histological images, inputting a test data set of digital histological images into the trained convolutional neural network, receiving as an output result of the convolutional neural network a probability value that the inputted test data set corresponds to the target tissue type.

Abstract: Methods and systems disclosed herein relate generally to systems and methods for predicting a future visual acuity of a subject by using machine-learning models. An image of at least part of a retina of a subject can be processed by one or more first machine-learning models to detect a set of retina-related segments. Segment-specific metrics that characterize a retina-related segment of the set of retina-related segments can be generated. The segment-specific metrics can be processed by using a second machine-learning model to generate a result corresponding to a prediction corresponding to a future visual acuity of the subject.