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.

Date of Patent:
Nov. 21, 2023

Filed:

Dec. 28, 2020
Applicant:

Hitachi, Ltd., Tokyo, JP;

Inventors:

Masahiro Ogino, Tokyo, JP;

Zisheng Li, Tokyo, JP;

Yukio Kaneko, Tokyo, JP;

Assignee:

Hitachi, Ltd., Tokyo, JP;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 6/00 (2006.01); G06T 7/00 (2017.01); G16H 30/20 (2018.01); G06N 20/00 (2019.01); G06N 5/02 (2023.01); A61B 5/055 (2006.01); A61B 5/00 (2006.01); A61B 6/03 (2006.01); A61B 8/00 (2006.01); A61B 8/08 (2006.01); G06F 18/21 (2023.01); G06V 10/82 (2022.01); G06V 10/44 (2022.01);
U.S. Cl.
CPC ...
A61B 6/5217 (2013.01); A61B 5/055 (2013.01); A61B 5/7267 (2013.01); A61B 5/7275 (2013.01); A61B 5/7485 (2013.01); A61B 6/032 (2013.01); A61B 6/469 (2013.01); A61B 8/469 (2013.01); A61B 8/5223 (2013.01); G06F 18/217 (2023.01); G06N 5/02 (2013.01); G06N 20/00 (2019.01); G06T 7/0012 (2013.01); G06V 10/82 (2022.01); G16H 30/20 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20081 (2013.01); G06V 10/454 (2022.01); G06V 2201/03 (2022.01);
Abstract

To obtain a predictive model that shows a diagnostic prediction result with higher accuracy and high medical validity. A medical imaging apparatus includes an imaging unit that collects an image signal of an inspection target, and an image processing unit that generates first image data from the image signal and performs image processing of the first image data. The image processing unit includes a feature quantity extraction unit that extracts a first feature quantity from the first image data, a feature quantity abstraction unit that abstracts the first feature quantity to extract a second feature quantity, a feature quantity conversion unit that converts the second feature quantity into a third feature quantity extracted by second image data, and an identification unit that uses the converted third feature quantity to calculate a predetermined parameter value.


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