Feng Gao

US Patent:

6541263, Apr 1, 2003

Filed:

Feb 21, 2001

Appl. No.:

09/788353

Inventors:

Feng Gao - Stamford CT

Assignee:

Euro-Celtique, S.A. Luxembourg

International Classification:

G01N 3392

US Classification:

436 71, 436101, 436173, 436139

Abstract:

The present invention is directed to a method of detecting a corticosteroid in a sample by adding an internal standard to a sample suspected of containing a corticosteroid; removing interfering compounds from the sample; placing the sample on an HPLC column equilibrated with a NH OAc:MeOH solution and collecting an eluent; and analyzing the eluent of the HPLC column with a MS, wherein if contained in the sample, the corticosteroid forms an adduct that is detected by the MS.

US Patent:

6740641, May 25, 2004

Filed:

Jul 22, 2002

Appl. No.:

10/199526

Inventors:

Feng Gao - Stamford CT
Jahanara Miotto - Carmel NY

Assignee:

Euro-Celtique, S.A.

International Classification:

A01N 4304

US Classification:

514 27, 514281, 514282, 546 44, 546 45, 546 46, 424 7829

Abstract:

Glucoside and glucuronide derivatives of hydromorphone, dihydromorphine, and dihydroisomorphine and pharmaceutically acceptable salts thereof; pharmaceutical compositions comprising a glucoside or glucuronide derivative of hydromorphone, dihydromorphine, or dihydroisomorphine or a pharmaceutically acceptable salt thereof; and methods for treating or preventing pain in a patient comprising administering to a patient in need thereof a glucoside or glucuronide derivative of hydromorphone, dihydromorphine, or dihydroisomorphine or a pharmaceutically acceptable salt thereof are disclosed.

US Patent:

20190050981, Feb 14, 2019

Filed:

Jun 2, 2018

Appl. No.:

15/996434

Inventors:

- Shenzhen, CN
Shanhui Sun - Princeton NJ, US
Hanbo Chen - Seattle WA, US
Junjie Bai - Seattle WA, US
Feng Gao - Seattle WA, US
Youbing Yin - Kenmore WA, US

Assignee:

SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION - Shenzhen

International Classification:

G06T 7/00
G06T 7/11
G06N 3/04
G06N 5/04
G06N 99/00
G06T 11/20
G16H 30/40

Abstract:

A computer-implemented method for automatically detecting a target object from a 3D image is disclosed. The method may include receiving the 3D image acquired by an imaging device. The method may further include detecting, by a processor, a plurality of bounding boxes as containing the target object using a 3D learning network. The learning network may be trained to generate a plurality of feature maps of varying scales based on the 3D image. The method may also include determining, by the processor, a set of parameters identifying each detected bounding box using the 3D learning network, and locating, by the processor, the target object based on the set of parameters.

US Patent:

20190050982, Feb 14, 2019

Filed:

Jul 5, 2018

Appl. No.:

16/028389

Inventors:

- Shenzhen, CN
Shanhui Sun - Princeton NJ, US
Feng Gao - Seattle WA, US
Junjie Bai - Seattle WA, US
Hanbo Chen - Seattle WA, US
Youbing Yin - Kenmore WA, US

Assignee:

SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION - Shenzhen

International Classification:

G06T 7/00
G06T 7/70

Abstract:

The present disclosure is directed to a method and system for automatically detecting a physiological condition from a medical image of a patient. The method may include receiving the medical image acquired by an imaging device. The method may further include detecting, by a processor, target objects and obtaining the corresponding target object patches from the received medical image. And the method may further include determining, by the processor, a first parameter using a first learning network for each target object patch. The first parameter represents the physiological condition level of the corresponding target object, and the first learning network is trained by adding one or more auxiliary classification layers. This method can quickly, accurately, and automatically predict target object level and/or image (patient) level physiological condition from a medical image of a patient by means of a learning network, such as 3D learning network.