Nvidia Corporation

National Institutes of Health, a Component of the US Dept. of Health & Human Services

Xiaosong Wang

Daguang Xu

Ziyue Xu

Lickkong Tam

Dong Yang

Le Lu

Ronald M Summers

Wenqi Li

Fausto Milletari

Ling Zhang

Ali Hatamizadeh

Liyue Shen

Holger Roth

Wentao Zhu

Yuhong Wen

Riddhish Bhalodia

Zhiyong Lu

Can Zhao

Kevin Frank Lu

Yifan Peng

Apparatuses, systems, and techniques to generate one or more data items related to an object from a set of multiple data items related to the same object using a generative adversarial network. In at least one embodiment, data objects in a set are disentangled into common and unique components and encoded such that they may be used to train one or more neural networks to generate missing data from the data objects in the set, such as generating missing medical images from a set of related medical images.

Representational disentanglement for multi-modality image completion using neural networks

Apparatuses, systems, and techniques to detect object in images including digital representations of those objects. In at least one embodiment, one or more objects are detected in an image based, at least in part, on points corresponding to a surface of one or more objects.

Object detection using one or more neural networks

Apparatuses, systems, and techniques to indicate an extent, to which text corresponds to one or more images. In at least one embodiment, an extent to which text corresponds to one or more images is indicated using one or more neural networks and used to train the one or more neural networks.

Pretraining framework for neural networks

Apparatuses, systems, and techniques to select labels of training images to train a network. In at least one embodiment, one or more labels of training images are selected to train a network.

Selecting annotations for training images using a neural network

Apparatuses, systems, and techniques to generate labeled training data. In at least one embodiment, labeled training images are generated from medial images annotated with natural language text.

Extending supervision using machine learning

A new chest X-ray database, referred to as 'ChestX-ray8', is disclosed herein, which comprises over 100,000 frontal view X-ray images of over 32,000 unique patients with the text-mined eight disease image labels (where each image can have multi-labels), from the associated radiological reports using natural language processing. We demonstrate that these commonly occurring thoracic diseases can be detected and spatially-located via a unified weakly supervised multi-label image classification and disease localization framework, which is validated using our disclosed dataset.

Method and system of building hospital-scale chest X-ray database for entity extraction and weakly-supervised classification and localization of common thorax diseases

Growing community of inventors

Rockville, MD, United States of America

Xiaosong Wang