Nvidia Corporation

The United States of America, As Represented by the Secretary, Department of Health and Human Services

Ziyue Xu

Daguang Xu

Dong Yang

Xiaosong Wang

Holger Reinhard Roth

Fausto Milletari

Ling Zhang

Lickkong Tam

Le Lu

Ronald M Summers

Adam Patrick Harrison

Daniel Joseph Mollura

Te-Chung Isaac Yang

Apparatuses, systems, and techniques to dynamically identify a neural network architecture for a data point. In at least one embodiment, a data point is fed to a plurality of neural network architectures, in which an optimal neural network architecture is determined based at least in part on information inferenced using the plurality of neural network architectures.

Technique to perform neural network architecture search

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

Methods include processing image data through a plurality of network stages of a progressively holistically nested convolutional neural network, wherein the processing the image data includes producing a side output from a network stage m, of the network stages, where m&#x3e;1, based on a progressive combination of an activation output from the network stage m and an activation output from a preceding stage m&#x2212;1. Image segmentations are produced. Systems include a 3D imaging system operable to obtain 3D imaging data for a patient including a target anatomical body, and a computing system comprising a processor, memory, and software, the computing system operable to process the 3D imaging data through a plurality of progressively holistically nested convolutional neural network stages of a convolutional neural network.

Progressive and multi-path holistically nested networks for segmentation

In at least one embodiment, a reinforcement-learning-based searching approach is used to produce a training configuration for a machine-learning model. In at least one embodiment, 3D medical image segmentation is performed using learned image preprocessing parameters.

Growing community of inventors

Reston, VA, United States of America

Ziyue Xu