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:
Feb. 10, 2026

Filed:

Feb. 02, 2023
Applicant:

Xidian University, Xi'an, CN;

Inventors:

Xueli Chen, Xi'an, CN;

Huan Kang, Xi'an, CN;

Hui Xie, Xi'an, CN;

Duofang Chen, Xi'an, CN;

Shenghan Ren, Xi'an, CN;

Wangting Zhou, Xi'an, CN;

Assignee:

Xidian University, Xi'an, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 3/4053 (2024.01); G02B 21/26 (2006.01); G02B 21/36 (2006.01); G06T 3/4007 (2024.01); G06T 3/4046 (2024.01); G06T 7/00 (2017.01); G06V 10/26 (2022.01); G06V 10/74 (2022.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01); H04N 23/56 (2023.01); H04N 23/955 (2023.01); H04N 25/76 (2023.01);
U.S. Cl.
CPC ...
G06T 3/4053 (2013.01); G02B 21/26 (2013.01); G02B 21/365 (2013.01); G06T 3/4007 (2013.01); G06T 3/4046 (2013.01); G06T 7/0012 (2013.01); G06V 10/26 (2022.01); G06V 10/761 (2022.01); G06V 10/763 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01); H04N 23/56 (2023.01); H04N 23/955 (2023.01); H04N 25/76 (2023.01); G06T 2207/10056 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20132 (2013.01); G06T 2207/20192 (2013.01); G06T 2207/30024 (2013.01); G06V 2201/03 (2022.01);
Abstract

A large-field-of-view, high-throughput and high-resolution pathological section analyzer includes an image collector for collecting a set of computing microscopic images of a pathological section sample; a data preprocessing circuit for iteratively updating the set of computing microscopic images by a multi-height phase recovery algorithm to obtain a low-resolution reconstructed image; an image super-resolution circuit for super-resolving the low-resolution reconstructed image according to a pre-trained super-resolution model to obtain a high-resolution reconstructed image; and an image analysis circuit for automatically analyzing the high-resolution reconstructed image according to different tasks, and specifically selecting different analysis models according to the different tasks to obtain corresponding auxiliary diagnosis results. Imaging visual field of the pathological section analyzer is hundreds of times that of the traditional optical microscope, a deep learning network is adopted to analyze pathological conditions of unstained pathological sections, so that the analysis process of pathological sections is simplified.


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