公开(公告)号：US20230192882A1

公开(公告)日：2023-06-22

申请号：US17955743

申请日：2022-09-29

Applicant: Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

Inventor： Daowen WANG ,  Chen CHEN ,  Jiangang JIANG ,  Jin WANG ,  Yan ZHUANG ,  Huihui LI ,  Yanghui CHEN ,  Mengying HE

IPC: C07K16/28 ,  G01N33/68 ,  G01N33/543 ,  A61P9/04

CPC classification number: C07K16/2896 ,  G01N33/6863 ,  G01N33/6872 ,  G01N33/54386 ,  A61P9/04 ,  G01N2800/325

Abstract: The invention “use of biomarker or biomarkers combination in preparing a diagnostic reagent for fulminant myocarditis and a drug for fulminant myocarditis” belongs to the field of disease diagnostic reagents and drugs. The biomarker comprises Siglec-5. The biomarkers combination is further selected from the group consisting of sST2, PAI-1, Siglec-5, CD163, CD40, P-Cadherin, CD14, CTLA4. The invention finds that the levels of the biomarker in plasma of patients with fulminant myocarditis is increased, and proves that the biomarker plays a role in diagnosing fulminant myocarditis and a role as a drug target in relieving and/or improving fulminant myocarditis. The level of the biomarker can be detected to forecast or auxiliary diagnose fulminant myocarditis or estimate prognosis of fulminant myocarditis. Meanwhile, level of the biomarkers is controlled through drugs, more intensified diagnosis and follow-up treatment can be carried out on diseases, and the biomarkers have huge clinical application value.

公开(公告)号：US20170278220A1

公开(公告)日：2017-09-28

申请号：US15126271

申请日：2016-03-17

Applicant: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Lerenhan LI ,  Nong SANG ,  Changxin GAO ,  Luxin YAN ,  Jin WANG ,  Shiwei ZHANG ,  Yuanjie SHAO ,  Juncai PENG

IPC: G06T5/00 ,  G06T7/00 ,  G06T3/40

CPC classification number: G06T5/002 ,  G06T3/4007 ,  G06T3/4076 ,  G06T7/0002 ,  G06T2207/10048 ,  G06T2207/20076

Abstract: The invention discloses a method for correcting aero-optical thermal radiation noise, comprising steps of: pretreating a degraded image to obtain a multi-scale degraded image group, conducting iteration process of obtaining an optimal solution by using a last scale estimation result as an original value of next scale estimation according to the multi-scale degraded image group, thereby facilitating original-scale bias field estimation, and restoring the degraded image according to the original-scale bias field estimated value thereby obtaining an image after aero-optical thermal radiation noise correction. The invention also discloses a system for correcting aero-optical thermal radiation noise. The invention is capable of solving problems with conventional methods, comprising poor correction effect, high complexity, and incapability in correcting the thermal radiation noise at an image level, and applicable to restoration of an image with aero-optical thermal radiation noise.

公开(公告)号：US20180068430A1

公开(公告)日：2018-03-08

申请号：US15709454

申请日：2017-09-19

Applicant: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Nong SANG ,  Lerenhan LI ,  Luxin YAN ,  Changxin GAO ,  Yuanjie SHAO ,  Juncai PENG ,  Shiwei ZHANG ,  Jin WANG

IPC: G06T7/00 ,  G06N3/02 ,  G06F17/11 ,  G06F17/18

CPC classification number: G06T7/0002 ,  G06F17/11 ,  G06F17/18 ,  G06K9/40 ,  G06K9/4628 ,  G06N3/02 ,  G06T5/003 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: A method for estimating a blur kernel size, the method including: (1) pre-processing a blurred image, to obtain an image, so that a size of the image satisfies an image input size of a multi-class convolutional neural network (CNN); (2) inputting the image into a multi-class CNN with completed training, to obtain a blur-kernel-size probability distribution vector; and (3) comparing each element in the blur-kernel-size probability distribution vector, so that an estimated blur kernel size of the blurred image is the blur kernel size corresponding to a largest element. The invention also provides a system for estimating a blur kernel size. The system includes an image pre-processing module, a training-set synthesizing module, a multi-class CNN module, and a blur kernel size estimation module.