Arizona State University

Dignity Health

Yezhou Yang

Marco Santello

Mark C Preul

Evgenii Belykh

Mohammadhassan Izadyyazdanabadi

Qiushi Fu

Changhoon Kim

Yi Ren

A system and associated methods for decentralized attribution of GAN models is disclosed. Given a group of models derived from the same dataset and published by different users, attributability is achieved when a public verification service associated with each model (a linear classifier) returns positive only for outputs of that model. Each model is parameterized by keys distributed by a registry. The keys are computed from first-order sufficient conditions for decentralized attribution. The keys are orthogonal or opposite to each other and belong to a subspace dependent on the data distribution and the architecture of the generative model.

Systems and methods for decentralized attribution of generative models

In accordance with some embodiments of the disclosed subject matter, systems, methods, and media for automatically transforming a digital image into a simulated pathology image are provided. In some embodiments, the method comprises: receiving a content image from an endomicroscopy device; receiving, from a hidden layer of a convolutional neural network (CNN) trained to recognize a multitude of classes of common objects, features indicative of content of the content image; receiving, providing a style reference image to the CNN; receiving, from another hidden layer of the CNN, features indicative of a style of the style reference image; receiving, from the hidden layers of the CNN, features indicative of content and style of a target image; generating a loss value based on the features of the content image, the style reference image, and the target image; minimizing the loss value; and displaying the target image with the minimized loss.

Systems, methods, and media for automatically transforming a digital image into a simulated pathology image

Methods and systems are presented for kinematic tracking and assessment of upper extremity function of a patient. A sequence of 2D images is captured by one or more cameras of a patient performing an upper extremity function assessment tasks. The captured images are processed to separately track body movements in 3D space, hand movements, and object movements. The hand movements are tracked by adjusting a position, orientation, and finger positions of a three-dimensional virtual model of a hand to match the hand in each 2D image. Based on the tracked movement data, the system is able to identify specific aspects of upper extremity function that exhibit impairment instead of providing only a generalized indication of upper extremity impairment.

Computer-vision-based clinical assessment of upper extremity function

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Tempe, AZ, United States of America

Yezhou Yang