The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jan. 03, 2017
Filed:
Oct. 20, 2015
Maryellen L. Giger, Elmhurst, IL (US);
Robert Tomek, Chicago, IL (US);
Jeremy Bancroft Brown, Chicago, IL (US);
Andrew Robert Jamieson, Chicago, IL (US);
LI Lan, Hinsdale, IL (US);
Michael R. Chinander, Chicago, IL (US);
Karen Drukker, Crete, IL (US);
Hui LI, Naperville, IL (US);
Neha Bhooshan, Potomac, MD (US);
Gillian Newstead, Chicago, IL (US);
Maryellen L. Giger, Elmhurst, IL (US);
Robert Tomek, Chicago, IL (US);
Jeremy Bancroft Brown, Chicago, IL (US);
Andrew Robert Jamieson, Chicago, IL (US);
Li Lan, Hinsdale, IL (US);
Michael R. Chinander, Chicago, IL (US);
Karen Drukker, Crete, IL (US);
Hui Li, Naperville, IL (US);
Neha Bhooshan, Potomac, MD (US);
Gillian Newstead, Chicago, IL (US);
QUANTITATIVE INSIGHTS, INC., Chicago, IL (US);
Abstract
Computerized interpretation of medical images for quantitative analysis of multi-modality breast images including analysis of FFDM, 2D/3D ultrasound, MRI, or other breast imaging methods. Real-time characterization of tumors and background tissue, and calculation of image-based biomarkers is provided for breast cancer detection, diagnosis, prognosis, risk assessment, and therapy response. Analysis includes lesion segmentation, and extraction of relevant characteristics (textural/morphological/kinetic features) from lesion-based or voxel-based analyses. Combinations of characteristics in several classification tasks using artificial intelligence is provided. Output in terms of 1D, 2D or 3D distributions in which an unknown case is identified relative to calculations on known or unlabeled cases, which can go through a dimension-reduction technique. Output to 3D shows relationships of the unknown case to a cloud of known or unlabeled cases, in which the cloud demonstrates the structure of the population of patients with and without the disease.