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公开(公告)号：US12154036B2
公开(公告)日：2024-11-26
申请号：US16999118
申请日：2020-08-21
Inventor： Shuqiang Wang , Yanyan Shen , Wenyong Zhang
IPC: G06N3/088 , G06F18/214 , G06F18/241 , G06F18/2431 , G06N3/044 , G06N3/045 , G06N3/08
Abstract: The present disclosure relates to an enhanced generative adversarial network and a target sample recognition method. The enhanced generative adversarial network in the present disclosure includes at least one enhanced generator and at least one enhanced discriminator, where the enhanced generator obtains generated data by processing initial data, and provides the generated data to the enhanced discriminator; the enhanced discriminator processes the generated data and feeds back a classification result to the enhanced generator; the enhanced discriminator includes: a convolution layer, a basic capsule layer, a convolution capsule layer, and a classification capsule layer, and the convolution layer, the basic capsule layer, the convolution capsule layer, and the classification capsule layer are sequentially connected to each other.
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Patent Agency Ranking
公开(公告)号：US12254684B2
公开(公告)日：2025-03-18
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：US10748080B2
公开(公告)日：2020-08-18
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: A method for processing tensor data for pattern recognition and a computer device are provided. The method includes: constructing a decision function by the optimal projection tensor W which has been rank-one decomposed together with the offset scalar b, and inputting to-be-predicted tensor data which has been rank-one decomposed into the decision function for prediction.
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公开(公告)号：US12159706B2
公开(公告)日：2024-12-03
申请号：US17764755
申请日：2021-02-07
Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY
Inventor： Shuqiang Wang , Junren Pan , Yanyan Shen
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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公开(公告)号：US11270526B2
公开(公告)日：2022-03-08
申请号：US16623397
申请日：2017-08-07
Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES , SHENZHEN SIBIKU TECHNOLOGY CO., LTD
Inventor： Shuqiang Wang , Yongcan Wang , Yue Yang , Yanyan Shen , Minghui Hu
Abstract: A teaching assistance method and a teaching assistance system using said method, the teaching assistance method comprising implementing behaviour detection of students in classroom images by means of using a trained depth tensor column network model, thus providing higher image recognition precision and reducing the hardware requirements for algorithms, and being able to be used on an embedded device, reducing the usage costs of the teaching assistance method; in addition, a teaching assistance system using said teaching assistance method has the same advantages.
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6.发明授权公开(公告)号：US11972604B2
公开(公告)日：2024-04-30
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: G06V10/82 , G06V10/774
CPC classification number: G06V10/82 , G06V10/7747
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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