公开(公告)号：US20240029866A1

公开(公告)日：2024-01-25

申请号：US17764755

申请日：2021-02-07

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Shuqiang WANG ,  Junren PAN ,  Yanyan SHEN

IPC: G16H30/40 ,  G16H50/20 ,  G06T7/00

CPC classification number: G16H30/40 ,  G16H50/20 ,  G06T7/0012 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016

Abstract: The present application provides an image-driven brain atlas construction method and apparatus, a device and a storage medium, and involves in the field of medical imaging technologies. The method includes: acquiring a node feature matrix, where the node feature matrix includes time sequences of multiple nodes of a brain; performing hypergraph data structure transformation on the node feature matrix to acquire a first hypergraph incidence matrix; inputting the first hypergraph incidence matrix and the node feature matrix into a trained hypergraph transition matrix generator for processing to output and acquire a first hypergraph transition matrix, where the first hypergraph transition matrix characterizes a constructed multi-modal brain atlas. The technical solution provided by the present application can construct the multi-modal brain atlas, and this multi-modal brain connection structure can express more feature information. When it is applied to the brain disease diagnosis process, the accuracy of disease diagnosis can be improved.

公开(公告)号：US20220148293A1

公开(公告)日：2022-05-12

申请号：US17283199

申请日：2020-03-11

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Shuqiang WANG ,  Wen YU ,  Chenchen XIAO ,  Shengye HU ,  Yanyan SHEN

IPC: G06V10/774 ,  G06V10/82 ,  G06N3/04 ,  G06T7/00

Abstract: An image feature visualization method and apparatus, and an electronic device during model training, inputs the real training data with positive samples into a mapping generator to obtain fictitious training data with negative samples. The mapping generator includes a mapping module configured to learn a key feature map that distinguishes the real training data with positive samples/negative samples, and the fictitious training data with negative samples is generated based on the real training data with positive samples and the key feature map. The training data with negative samples is input into a discriminator to obtain a discrimination result. An optimizer optimizes the mapping generator and the discriminator until training is completed. During model application, a target image that is to be processed is input into the mapping generator, and the mapper in the mapping generator extracts features of the target image.

公开(公告)号：US20230342918A1

公开(公告)日：2023-10-26

申请号：US17764446

申请日：2021-02-03

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Shuqiang WANG ,  Qiankun ZUO ,  Min GAN

IPC: G06T7/00 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G16H50/20 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/30016 ,  G06T2207/20084

Abstract: The present application is applicable to the field of medical imaging technologies, and provides an image-driven brain atlas construction method and apparatus, a device and a storage medium. The method includes: acquiring multi-modal data of a brain to be predicted, where the multi-modal data is acquired according to image data collected when the brain is under at least three different modalities; inputting the multi-modal data into a preset fusion network for processing to output and acquire feature parameters of the brain; where the processing of the multi-modal data by the fusion network includes: extracting a non-Euclidean spacial feature and an Euclidean spacial feature of the multi-modal data, and performing hypergraph fusion on the non-Euclidean spacial feature and the Euclidean spacial feature to acquire the feature parameters, where the feature parameters are used to characterize a brain connection matrix and/or a disease category of the brain.

公开(公告)号：US20170344906A1

公开(公告)日：2017-11-30

申请号：US15310330

申请日：2015-12-04

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Shuqiang WANG ,  Dewei ZENG ,  Yanyan SHEN ,  Changhong SHI ,  Zhe LU

IPC: G06N99/00 ,  G06F17/16

Abstract: Optimization method and system for supervised learning under tensor mode is provided; wherein the method includes: receiving an input training tensor data set; introducing a within class scatter matrix into an objective function such that between class distance is maximized, at the same time, within class distance is minimized by the objective function; constructing an optimal frame of the objective function of an optimal projection tensor machine OPSTM subproblem; constructing an optimal frame of an objective function of an OPSTM problem; solving the revised dual problem and outputting alagrangian optimal combination and an offset scalar b; calculating a projection tensor W*; calculating a optimal projection tensor W; by the W together with the b, constructing a decision function; inputting to-be-predicted tensor data which has been rank-one decomposed into the decision function for prediction. This overcomes issues such as curse of dimensionality, over learning and small sample occurred when vector mode algorithms process the tensor data, and effectively avoids a time-consuming alternative projection iterative process of the tensor mode algorithms of the prior art.

公开(公告)号：US20220343638A1

公开(公告)日：2022-10-27

申请号：US17763513

申请日：2019-11-19

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Shuqiang WANG ,  Wen YU ,  Yanyan SHEN ,  Zhuo CHEN

IPC: G06V10/80 ,  G06V10/82 ,  G06V10/20 ,  G06T7/00 ,  G16H30/40

Abstract: The present application is suitable for use in the technical field of computers, and provides a smart diagnosis assistance method and terminal based on medical images, comprising: acquiring a medical image to be classified; pre-processing the medical image to be classified to obtain a pre-processed image; and inputting the pre-processed image into a trained classification model for classification processing to obtain a classification type corresponding to the pre-processed image, the classification model comprising tensorized network layers and a second-order pooling module. As the trained classification model comprises tensor decomposed network layers and a second-order pooling module, when processing images on the basis of the classification model, more discriminative features related to pathologies can be extracted, increasing the accuracy of medical image classification.

公开(公告)号：US20220101527A1

公开(公告)日：2022-03-31

申请号：US17549258

申请日：2021-12-13

Applicant: Shenzhen Institutes of Advanced Technology

Inventor： Shuqiang WANG ,  Wen YU ,  Chenchen XIAO ,  Shengye HU

IPC: G06T7/00 ,  G06V10/764 ,  G06V10/774

Abstract: A visualization method for evaluating brain addiction traits, an apparatus, and a computer-readable storage medium are provided. The method includes the following. A visualization processing request is received from a client, where the visual processing request contains an image to-be-processed. The image to-be-processed is masked to obtain a perturbation image masked. The perturbation image is classified with a visualization processing model to obtain a classification result, and the classification result is calculated to obtain an evaluation value of the perturbation image, where the evaluation value of the perturbation image is less than an evaluation value of the image to-be-processed without masking. The visualization evaluation result is determined according to the evaluation value of the perturbation image. The visualization evaluation result is sent to the client.

公开(公告)号：US20230343026A1

公开(公告)日：2023-10-26

申请号：US18026498

申请日：2021-01-08

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Shuqiang WANG ,  Bowen HU ,  Yanyan SHEN

IPC: G06T9/00 ,  G06T19/20 ,  G06T17/00

CPC classification number: G06T17/00 ,  G06T9/002 ,  G06T19/20 ,  G06T2210/41 ,  G06T2219/2004

Abstract: A method and a device for a three-dimensional reconstruction of brain structure, and terminal equipment. The method includes steps of: obtaining a 2D image of a brain, inputting the 2D image of the brain into a 3D brain point-cloud reconstruction model that has been trained to be processed, and outputting a 3D point-cloud of the brain. The 3D brain point-cloud reconstruction model includes a ResNet encoder and a graphic convolutional neural network. The ResNet encoder is configured to extract a coding feature vector of the 2D image of the brain, and the graphic convolutional neural network is configured to construct the 3D point-cloud of the brain according to the coding feature vector.

公开(公告)号：US20230022206A1

公开(公告)日：2023-01-26

申请号：US17786840

申请日：2019-12-17

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Shuqiang WANG ,  Senrong YOU ,  Guobao WU ,  Yiqian LU ,  Fen MIAO ,  Chitang ZHANG

IPC: G06T7/00 ,  G06V10/774 ,  G06V10/764 ,  G06V10/82 ,  A61B5/00 ,  A61B5/08

Abstract: An infrared image sequence-based sleep quality evaluation system and method. The method comprises: obtaining a plurality of respiratory infrared image sequences to be evaluated, one respiratory infrared image sequence comprising a plurality of respiratory infrared image frames to be evaluated; performing sleep quality evaluation on each respiratory infrared image sequence in the plurality of respiratory infrared image sequences by means of a classifier to obtain a sleep quality evaluation result corresponding to each respiratory infrared image sequence; and counting the number of different sleep quality evaluation results according to the sleep quality evaluation results respectively corresponding to the plurality of respiratory infrared image sequences, and determining the sleep quality evaluation result with the largest number as a sleep quality evaluation result of a user. Contactless sleep monitoring can be carried out on a user, monitoring costs are reduced at the same time, and evaluation accuracy of sleep quality is improved.

公开(公告)号：US20220414849A1

公开(公告)日：2022-12-29

申请号：US17779718

申请日：2019-11-25

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Shuqiang WANG ,  Senrong YOU ,  Yiqian LU ,  Shengye HU

IPC: G06T5/10 ,  G06N3/08

Abstract: Disclosed by the present application are an image enhancement method and apparatus, a terminal device and a computer-readable storage medium. The image enhancement method comprises: obtaining an image to be processed; performing a wavelet transform operation on the image to obtain raw feature information of the image, the raw feature information comprising global contour feature information, transversal detail feature information, longitudinal detail feature information, and contrast detail feature information; inputting the raw feature information into a trained target network for processing to obtain corresponding reconstruction feature information, the reconstruction feature information comprising global contour reconstruction information, transversal detail reconstruction information, longitudinal detail reconstruction information, and contrast detail reconstruction information; performing an inverse wavelet transform operation on the reconstruction feature information to obtain a reconstructed image; the resolution of the reconstructed image is higher than the resolution of the image to be processed.