Richard S Ha

20210239450, Aug 5, 2021

Jan 29, 2021

17/162720

- New York NY, US
RICHARD HA - White Plains NY, US
DIANA MOJAHED - New York NY, US
HANINA HIBSHOOSH - New York NY, US
JAMES MCLEAN - New York NY, US

G01B 9/02
A61B 3/10
H01L 33/00

An exemplary system for generating an image(s) of a sample(s) can include, for example, an imaging arrangement that can include a superluminescent diode (SLD) configured to generate a radiation(s) to be provided to the sample(s), and a spectrometer configured to (i) sample an A-line sampling rate of at least about 200kHz, (ii) receive a resultant radiation from the sample(s) based on the sampling rate, and (iii) generate information based on the resultant radiation, and a computer hardware arrangement configured to generate the image(s) of the sample(s) based on the information received from the spectrometer. The imaging arrangement can be an interferometric imaging arrangement, which can be an optical coherence tomography imaging (OCT) arrangement. The computer hardware arrangement can be further configured to facilitate a plurality of b-scan acquisitions of the sample(s) and facilitate the b-scan acquisitions in order to generate the image(s).

20210074411, Mar 11, 2021

Nov 17, 2020

16/950043

- New York NY, US
Richard HA - White Plains NY, US

G16H 30/40
G16H 50/20
G06T 7/00

An exemplary system, method and computer-accessible medium for determining ductal carcinoma in situ (DCIS) information regarding a patient(s) can include for example, receiving image(s) of internal portion(s) of a breast of the patient(s), and automatically determining the DCIS information by applying a neural network(s) to the image(s). The DCIS information can include predicting (i) pure DCIS or (ii) DCIS with invasion. Input information of the patient(s) can be selected for a DCIS observation for determining the DCIS information. The image(s) can be a mammographic image(s). The image(s) can be one of a magnetic resonance image or a computer tomography image.

20200372636, Nov 26, 2020

Nov 21, 2018

16/766265

- NEW YORK NY, US
RICHARD HA - White Plains NY, US

G06T 7/00
G16H 50/30
G06T 5/50
G06T 3/40
G06T 3/60
G06N 3/04

An exemplary system, method and computer-accessible medium for determining a breast cancer response(s) for a patient(s) can include, for example, receiving an image(s) of an internal portion(s) of a breast of the patient(s), and determining the breast cancer response(s) by applying a neural network(s) to the image(s). The breast cancer response(s) can be a response to at least one chemotherapy treatment. The breast cancer response(s) can include an Oncotype DX recurrence score. The breast cancer response(s) can be a neoadjuvant axillary response. The image(s) can be a magnetic resonance image(s) (MRI). The MRI(s) can include a dynamic contrast enhanced MRI(s).

20200372637, Nov 26, 2020

Nov 23, 2018

16/766269

- hEW YORK NY, US
RICHARD HA - White Plains NY, US

G06T 7/00
G06T 7/11

An exemplary system, method and computer-accessible medium for classifying a tissue(s) of a patient(s) can include, for example, receiving an image(s) of an internal portion(s) of a breast of the patient(s), and automatically classifying the tissue(s) of the breast by applying a neural(s) network to the image(s). The tissue(s) can include a lymph node(s). The lymph node(s) can be classified as a cancerous tissue or a non-cancerous tissue. The tissue(s) can be classified as a fibroglandular tissue or a background parenchymal enhancement tissue. The tissue(s) can be classified as a cancer molecular subtype. The image(s) can be is a magnetic resonance image.

20200364855, Nov 19, 2020

Nov 21, 2018

16/766123

- NEW YORK NY, US
RICHARD HA - White Plains NY, US

G06T 7/00
G06K 9/62
G06T 11/00
G06T 7/10
G06T 3/40
G06N 3/08
G06N 3/04
G16H 50/20
G16H 30/40
A61B 5/00

An exemplary system, method and computer-accessible medium for classifying a breast tissue(s) a patient(s) can include, for example, receiving an image(s) of an internal portion(s) of a breast of the patient(s), and automatically classifying the breast tissue(s) of the breast by applying a neural network(s) to the image(s). The automatic classification can include a classification as to whether the breast tissue(s) is atypical ductal hyperplasia or ductal carcinoma. The automatic classification can include a classification as to whether the breast tissue(s) is a cancerous tissue or a non-cancerous tissue. The image(s) can be a mammographic image or an optical coherence tomography image.