Yan Ling Liu

US Patent:

7970767, Jun 28, 2011

Filed:

Apr 30, 2007

Appl. No.:

11/742244

Inventors:

Katharina Probst - Dyer IN, US
Rayid Ghani - Chicago IL, US
Andrew E. Fano - Lincolnshire IL, US
Marko Krema - Evanston IL, US
Yan Liu - New York City NY, US

Assignee:

Accenture Global Services Limited - Dublin

International Classification:

G06F 17/30

US Classification:

707739, 707777, 707796, 707803

Abstract:

One or more classification algorithms are applied to at least one natural language document in order to extract both attributes and values of a given product. Supervised classification algorithms, semi-supervised classification algorithms, unsupervised classification algorithms or combinations of such classification algorithms may be employed for this purpose. The at least one natural language document may be obtained via a public communication network. Two or more attributes (or two or more values) thus identified may be merged to form one or more attribute phrases or value phrases. Once attributes and values have been extracted in this manner, association or linking operations may be performed to establish attribute-value pairs that are descriptive of the product. In a presently preferred embodiment, an (unsupervised) algorithm is used to generate seed attributes and values which can then support a supervised or semi-supervised classification algorithm.

US Patent:

8521745, Aug 27, 2013

Filed:

Jun 13, 2011

Appl. No.:

13/158678

Inventors:

Katharina Probst - Dyer IN, US
Rayid Ghani - Chicago IL, US
Andrew E. Fano - Lincolnshire IL, US
Marko Krema - Evanston IL, US
Yan Liu - Elmsford NY, US

Assignee:

Accenture Global Services Limited - Dublin

International Classification:

G06F 17/30

US Classification:

707739

Abstract:

One or more classification algorithms are applied to at least one natural language document in order to extract both attributes and values of a given product. Supervised classification algorithms, semi-supervised classification algorithms, unsupervised classification algorithms or combinations of such classification algorithms may be employed for this purpose. The at least one natural language document may be obtained via a public communication network. Two or more attributes (or two or more values) thus identified may be merged to form one or more attribute phrases or value phrases. Once attributes and values have been extracted in this manner, association or linking operations may be performed to establish attribute-value pairs that are descriptive of the product. In a presently preferred embodiment, an (unsupervised) algorithm is used to generate seed attributes and values which can then support a supervised or semi-supervised classification algorithm.

US Patent:

8626801, Jan 7, 2014

Filed:

Aug 4, 2011

Appl. No.:

13/197906

Inventors:

Katharina Probst - Dyer IN, US
Rayid Ghani - Chicago IL, US
Andrew E. Fano - Lincolnshire IL, US
Marko Krema - Evanston IL, US
Yan Liu - Elmsford NY, US

Assignee:

Accenture Global Services Limited - Dublin

International Classification:

G06F 17/30

US Classification:

707803

Abstract:

One or more classification algorithms are applied to at least one natural language document in order to extract both attributes and values of a given product. Supervised classification algorithms, semi-supervised classification algorithms, unsupervised classification algorithms or combinations of such classification algorithms may be employed for this purpose. The at least one natural language document may be obtained via a public communication network. Two or more attributes (or two or more values) thus identified may be merged to form one or more attribute phrases or value phrases. Once attributes and values have been extracted in this manner, association or linking operations may be performed to establish attribute-value pairs that are descriptive of the product. In a presently preferred embodiment, an (unsupervised) algorithm is used to generate seed attributes and values which can then support a supervised or semi-supervised classification algorithm.

US Patent:

7996440, Aug 9, 2011

Filed:

Apr 30, 2007

Appl. No.:

11/742215

Inventors:

Katharina Probst - Chicago IL, US
Rayid Ghani - Chicago IL, US
Andrew E. Fano - Lincolnshire IL, US
Marko Krema - Evanston IL, US
Yan Liu - New York City NY, US

Assignee:

Accenture Global Services Limited - Dublin

International Classification:

G06F 17/30

US Classification:

707803, 704275

Abstract:

One or more classification algorithms are applied to at least one natural language document in order to extract both attributes and values of a given product. Supervised classification algorithms, semi-supervised classification algorithms, unsupervised classification algorithms or combinations of such classification algorithms may be employed for this purpose. The at least one natural language document may be obtained via a public communication network. Two or more attributes (or two or more values) thus identified may be merged to form one or more attribute phrases or value phrases. Once attributes and values have been extracted in this manner, association or linking operations may be performed to establish attribute-value pairs that are descriptive of the product. In a presently preferred embodiment, an (unsupervised) algorithm is used to generate seed attributes and values which can then support a supervised or semi-supervised classification algorithm.

US Patent:

20210312612, Oct 7, 2021

Filed:

Apr 3, 2020

Appl. No.:

16/839733

Inventors:

- Chicago IL, US
- Otawara-shi, JP
Yan Liu - Vernon Hills IL, US
Zhou Yu - Vernon Hills IL, US

Assignee:

The University of Chicago - Chicago IL
CANON MEDICAL SYSTEMS CORPORATION - Otawara-shi

International Classification:

G06T 7/00
A61B 6/03

Abstract:

A method and apparatus uses multi-material decomposition of three or more material components to generate material-component images from spectral images reconstructed from spectral computed tomography data. In three-component material decomposition e.g., the Mendonça method is used for multi-material decomposition when the attenuation values satisfy an assumed volume fraction condition (i.e., for a given voxel, the attenuation values are within a triangle having vertices given by unit volume fractions of three respective material components). However, when the volume fraction condition fails (e.g., the attenuation values are outside the triangle), either a shortest-Hausdorff-distance method or a closest-edge method is used for multi-material decomposition. For example, the attenuation values of the voxel are projected onto a lower-dimensional space (e.g., the space of a closest edge) and decomposed into a pair/single material component(s) of the lower-dimensional space.

US Patent:

20210290193, Sep 23, 2021

Filed:

Jun 4, 2021

Appl. No.:

17/339093

Inventors:

- Otawara-shi, JP
Ruoqiao ZHANG - Arlington Heights IL, US
Zhou YU - Wilmette IL, US
Yan LIU - Vernon Hills IL, US

Assignee:

CANON MEDICAL SYSTEMS CORPORATION - Otawara-shi

International Classification:

A61B 6/00
G06T 11/00
G06T 5/20
G06T 5/10
G06N 3/08
G06N 3/04
G06T 5/50

Abstract:

A deep learning (DL) network corrects/performs sinogram completion in computed tomography (CT) images based on complementary high- and low-kV projection data generated from a sparse (or fast) kilo-voltage (kV)-switching CT scan. The DL network is trained using inputs and targets, which respectively generated with and without kV switching. Another DL network can be trained to correct sinogram-completion errors in the projection data after a basis/material decomposition. A third DL network can be trained to correct sinogram-completion errors in reconstructed images based on the kV-switching projection data. Performance of the DL network can be improved by dividing a 3D convolutional neural network (CNN) into two steps performed by respective 2D CNNs. Further, the projection data and DLL can be divided into high- and low-frequency components to improve performance.

US Patent:

20200402644, Dec 24, 2020

Filed:

Sep 2, 2020

Appl. No.:

17/010313

Inventors:

- Otawara-shi, JP
Zhou YU - Glenview IL, US
Yan LIU - Vernon Hills IL, US

Assignee:

CANON MEDICAL SYSTEMS CORPORATION - Otawara-shi

International Classification:

G16H 30/20
G06T 5/00
G06T 5/50
G06T 11/00
G06T 7/00
G06K 9/62
G06K 9/68
G06K 9/66
G06N 3/08

Abstract:

A medical image processing apparatus according to an embodiment comprises a memory and processing circuitry. The memory is configured to store a plurality of neural networks corresponding to a plurality of imaging target sites, respectively, the neural networks each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired for the corresponding imaging target site. The processing circuitry is configured to process first data into second data using, among the neural networks, the neural network corresponding to the imaging target site for the first data, wherein the first data is input to the input layer and the second data is output from the output layer.

US Patent:

20200323508, Oct 15, 2020

Filed:

Jun 29, 2020

Appl. No.:

16/915722

Inventors:

- Otawara-shi, JP
Zhou YU - Vernon Hills IL, US
Yan LIU - Vernon Hills IL, US

Assignee:

CANON MEDICAL SYSTEMS CORPORATION - Otawara-shi

International Classification:

A61B 6/00
A61B 6/04
A61B 6/02

Abstract:

An apparatus and method are described using a forward model to correct pulse pileup in spectrally resolved X-ray projection data from photon-counting detectors (PCDs). To calibrate the forward model, which represents each order of pileup using a respective pileup response matrix (PRM), an optimization search determines the elements of the PRMs that optimize an objective function measuring agreement between the spectra of recorded counts affected by pulse pileup and the estimated counts generated using forward model of pulse pileup. The spectrum of the recorded counts in the projection data is corrected using the calibrated forward model, by determining an argument value that optimizes the objective function, the argument being either a corrected X-ray spectrum or the projection lengths of a material decomposition. Images for material components of the material decomposition are then reconstructed using the corrected projection data.