Dong Yang

US Patent:

20200219259, Jul 9, 2020

Filed:

Mar 18, 2020

Appl. No.:

16/822101

Inventors:

- Erlangen, DE
Mingqing Chen - Plainsboro NJ, US
Daguang Xu - Princeton NJ, US
Zhoubing Xu - Plainsboro NJ, US
Dong Yang - Somerset NJ, US

International Classification:

G06T 7/00
G06K 9/66
G06T 7/11
G06N 7/00
G06N 3/04
G06K 9/62
G06N 3/08

Abstract:

Methods and apparatus for automated medical image analysis using deep learning networks are disclosed. In a method of automatically performing a medical image analysis task on a medical image of a patient, a medical image of a patient is received. The medical image is input to a trained deep neural network. An output model that provides a result of a target medical image analysis task on the input medical image is automatically estimated using the trained deep neural network. The trained deep neural network is trained in one of a discriminative adversarial network or a deep image-to-image dual inverse network.

US Patent:

20180225822, Aug 9, 2018

Filed:

Jan 9, 2018

Appl. No.:

15/865581

Inventors:

- Erlangen, DE
Mingqing Chen - Plainsboro NJ, US
Daguang Xu - Princeton NJ, US
Zhoubing Xu - Plainsboro NJ, US
Shun Miao - Princeton NJ, US
Dong Yang - Somerset NJ, US
He Zhang - Edison NJ, US

International Classification:

G06T 7/00
G06T 7/10

Abstract:

Systems and methods are provided for performing medical imaging analysis. Input medical imaging data is received for performing a particular one of a plurality of medical imaging analyses. An output that provides a result of the particular medical imaging analysis on the input medical imaging data is generated using a neural network trained to perform the plurality of medical imaging analyses. The neural network is trained by learning one or more weights associated with the particular medical imaging analysis using one or more weights associated with a different one of the plurality of medical imaging analyses. The generated output is outputted for performing the particular medical imaging analysis.

US Patent:

20180225823, Aug 9, 2018

Filed:

Jan 11, 2018

Appl. No.:

15/868062

Inventors:

- Erlangen, DE
Mingqing Chen - Plainsboro NJ, US
Daguang Xu - Princeton NJ, US
Zhoubing Xu - Plainsboro NJ, US
Dong Yang - Somerset NJ, US

International Classification:

G06T 7/00
G06K 9/66
G06T 7/11

Abstract:

Methods and apparatus for automated medical image analysis using deep learning networks are disclosed. In a method of automatically performing a medical image analysis task on a medical image of a patient, a medical image of a patient is received. The medical image is input to a trained deep neural network. An output model that provides a result of a target medical image analysis task on the input medical image is automatically estimated using the trained deep neural network. The trained deep neural network is trained in one of a discriminative adversarial network or a deep image-to-image dual inverse network.

US Patent:

20160328841, Nov 10, 2016

Filed:

May 8, 2015

Appl. No.:

14/707418

Inventors:

- Munich, DE
Dong Yang - Edison NJ, US
David Liu - Franklin Park NJ, US
Shaohua Kevin Zhou - Plainsboro NJ, US

International Classification:

G06T 7/00
G06K 9/46
G06K 9/62

Abstract:

Systems and methods for training a region clustering forest include receiving a set of medical training images for a population of patients. A set of image patches is extracted from each image in the set of medical training images. A plurality of region clustering trees are generated each minimizing a loss function based on respective randomly selected subsets of the set of image patches to train the region clustering forest. Each of the plurality of region clustering trees cluster image patches at a plurality of leaf nodes and the loss function measures a compactness of the cluster of image patches at each leaf node in each of the plurality of region clustering trees.