Shenzhen Institutes of Advanced Technology

Chinese Academy of Sciences

Huawei Technologies Co., Limited

Shenzhen Institute of Advanced Technology

Shenzhen Sibiku Technology Co., Ltd

Shuqiang Wang

Shunwan Zhuang

Yanyan Shen

Shengye Hu

Wen Yu

Zhuo Chen

Dewei Zeng

Yiqian Lu

Senrong You

Cheng He

Lujia Pan

Yue Yang

Hong Bing Wu

Yongcan Wang

Haibo Wang

Chenchen Xiao

Fen Miao

Changhong Jing

Changhong Shi

Wenyong Zhang

Zhe Lu

Minghui Hu

Junren Pan

Qiankun Zuo

Min Gan

Guobao Wu

Chitang Zhang

Changwei Gong

Junli Jia

A brain network and brain addictive connectivity computing method and device are disclosed. The method includes: obtaining real brain functional magnetic resonance (fMRI) images associated with different labels; generating first brain topologies associated with the different labels based on at least one training real brain fMRI image sample; generating a brain addiction standard feature map based on an additive real brain fMRI image; determining an initial weight value of each of the first brain topologies according to the brain addiction standard feature map; training an addictive brain network analysis model based on the first brain topologies associated with different labels and the initial weight value of each first brain topology; inputting at least one testing real brain fMRI image sample into addictive brain network analysis model to generate first weighted brain topologies.

Brain network and brain addictive connectivity computing method and device

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.

Infrared image sequence-based sleep quality evaluation system and method comprising performing evaluation by a classifier and counting the sleep quality evaluation results to determine the largest number to judge good or poor sleep quality

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.

Image enhancement method and apparatus, and terminal device

A method and a system for generating a dynamic addictive neural circuit based on weakly supervised contrastive learning are disclosed. The method includes: based on a convolutional neural network, reducing a dimensionality of voxels of multiple groups of fMRI to attributes of brain region nodes, and generating multiple groups of dynamic brain connection maps containing time series based on the attributes of the brain region nodes; extracting spatio-temporal features of brain connections in the dynamic brain connection maps; inputting the spatio-temporal features into an abnormal connection detection network, calculating an abnormal probability of brain connections based on contrastive learning, and obtaining the brain connection with a highest abnormal probability at each time point; and generating the dynamic addictive neural circuit based on neuroscientific prior knowledge and the brain connection with the greatest probability of abnormality.

Method and system for generating a dynamic addictive neural circuits based on weakly supervised contrastive learning

The present application provides a method and an apparatus for reconstructing a medical image, and a method and an apparatus for training a medical image reconstruction network. The method for training a medical image reconstruction network includes: performing feature coding extraction on a real image sample to obtain a feature coding vector of the real image sample; performing, through an image reconstruction network, image reconstruction based on the feature coding vector to obtain a first image, and performing image reconstruction based on a first hidden layer vector of the real image sample to obtain a second image; and performing, through an image discrimination network, image discrimination on the real image sample, the first image, and the second image, and optimizing the image reconstruction network according to an image discrimination result.

Method and apparatus for reconstructing medical image

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.

Image-driven brain atlas construction method, apparatus, device and storage medium

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.

Smart diagnosis assistance method to solve results of inaccurate classification of image, and terminal based on medical images

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.

Image-driven brain atlas construction method, device and storage medium

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.

Enhanced generative adversarial network and target sample recognition method

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.

Visualization method for evaluating brain addiction traits, apparatus, and medium

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.

Image feature visualization method, image feature visualization apparatus, and electronic device

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.

Teaching assistance method and teaching assistance system using said method

The present disclosure provides a license plate recognition method, a device thereof, and a user equipment. In the method, a vehicle image is acquired and input to a vehicle searching convolutional neural network (CNN) to search whether there is a vehicle. Then, a license plate image is acquired and input to a license plate searching CNN to search whether there is a license plate. And the license plate image is input to a tensor neural network to detect abnormal or not. Then, a recognition image is acquired and input to judge an area number, letters, and numbers. And an area number image, a letter image, and a number image are output and input to their corresponding recognition CNNs respectively to recognize an area number, letters, and numbers and output respectively. Ultimately, a license plate number recognition result in a form of 'area number letter number' are output.

License plate recognition method, device thereof, and user equipment

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.

Method for processing tensor data for pattern recognition and computer device

A junk information filtering method, including: detecting an Hypertext Transfer Protocol (HTTP) request data packet set generated according to that a user visits a target uniform resource locator (URL) by using a browser of a client, where the HTTP request data packet set includes at least one junk information request data packet and at least one non-junk information request data packet; identifying the junk information request data packet in the HTTP request data packet set according to a preset classification rule; and preventing, according to a preset interception policy, a destination host corresponding to the junk information request data packet from returning a junk information response data packet to the client. The embodiments of the present invention further disclose a junk information filtering apparatus. According to the present invention, accuracy of web page junk information filtering can be improved and user traffic can be saved.

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Shenzhen, China

Shuqiang Wang