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Patent Agency Ranking
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
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.
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2.发明申请公开(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.
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公开(公告)号：US20220343638A1
公开(公告)日：2022-10-27
申请号：US17763513
申请日：2019-11-19
Inventor： Shuqiang WANG , Wen YU , Yanyan SHEN , Zhuo CHEN
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.
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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
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.
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5.发明公开公开(公告)号：US20230343026A1
公开(公告)日：2023-10-26
申请号：US18026498
申请日：2021-01-08
Inventor： Shuqiang WANG , Bowen HU , Yanyan SHEN
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.
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