Teresa T Wu4558 W Maryland Ave, Glendale, AZ 85301

2005024, Oct 27, 2005

Apr 11, 2005

11/102956

Teresa Wu - Cupertino CA, US

G06F013/36

710314000

A storage device made of flash memory module(s) and a storage device controller and a PCI Express interface unit, is implemented to be compatible with (1) either ATA, ATAPI, SCSI or proprietary specification, and (2) PCI Express platform such as, with then, ExpressCard Standard or PCI Express Card Specification or PCI Express Mini Card Specification. The device includes memory module(s), which can accept data transfer and configuration and status report to/from non-volatile solid-state memory herein referred to as flash memory module(s). The storage device controller and the PCI Express interface unit work together to provide (A) PCI Express interface functionality and compatibility, and (B) ATA, ATAPI or SCSI or proprietary programming interface functionality and compatibility, alone with common flash memory operations such as programming reading, writing, erasing, and data transferring from/to PCI Express host platform.

2022031, Oct 6, 2022

Mar 18, 2022

17/698750

YANZHE XU - Tempe AZ, US
TERESA WU - Gilbert AZ, US
FEI GAO - Sunnyvale CA, US

G06T 7/00
G06V 10/44
G06T 15/08
G06V 10/77

A system for blob detection using deep learning is disclosed. The system may include a non-transitory computer-readable storage medium configured to store a plurality of instructions thereon which, when executed by a processor, cause the system to train a U-Net and generate a probability map including a plurality of centroids of a plurality of corresponding blobs, derive two distance maps with bounded probabilities, apply Difference of Gaussian (DoG) with an adaptive scale constrained by the two distance maps with the bounded probabilities, and apply Hessian analysis and perform a blob segmentation.

2020041, Dec 31, 2020

Feb 26, 2019

16/975647

- Rochester MN, US
- Scottsdale AZ, US
J. Ross Mitchell - Scottsdale AZ, US
Nhan L. Tran - Peoria AZ, US
Jing Li - Tempe AZ, US
Teresa Wu - Gilbert AZ, US

G06T 7/00
G06T 7/11

Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme (“GBM”) are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.

2020034, Oct 29, 2020

Nov 19, 2018

16/764837

- Rochester MN, US
Jing Li - Tempe AZ, US
Kristin R. Swanson - Phoenix AZ, US
Teresa Wu - Gilbert AZ, US
Nathan Gaw - Tempe AZ, US
Hyunsoo Yoon - Tempe AZ, US

G06N 20/10
G06K 9/62
G16H 30/40
G16H 50/20
G06T 7/00

Described here are systems and methods for generating and implementing a hybrid machine learning and mechanistic model to produce biological feature maps, or other measurements of biological features, based on an input of multiparametric magnetic resonance or other images. The hybrid model can include a combination of a machine learning model and a mechanistic model that takes as an input multiparametric MRI, or other imaging, data to generate biological feature maps (e.g., tumor cell density maps), or other measures or predictions of biological features (e.g., tumor cell density). The hybrid models have capabilities of learning individual-specific relationships between imaging features and biological features.

2016020, Jul 21, 2016

Mar 28, 2016

15/082095

Teresa Wu - Gilbert AZ, US
Min Zhang - Tempe AZ, US

A61B 5/20
A61B 5/00
A61B 5/055

Methods and systems for identifying blobs, for example kidney glomeruli, are disclosed. A raw image may be smoothed via a difference of Gaussians filter, and a Hessian analysis may be conducted on the smoothed image to mark glomeruli candidates. Exemplary candidate features are identified, such as average intensity A, likelihood of blobness R, and flatness S. A clustering algorithm may be utilized to post prune the glomeruli candidates.