Chinese Academy of Sciences

Shenzhen Institutes of Advanced Technology

Shenzhen Institute of Advanced Technology

Shenzhen Sibiku Technology Co., Ltd

Yanyan Shen

Shuqiang Wang

Zhuo Chen

Shengye Hu

Wen Yu

Dewei Zeng

Qiong Zhang

Yue Yang

Yongcan Wang

Yucai Yao

Qianqian Shen

Xuxing Chen

Chenchen Xiao

Junren Pan

Minghui Hu

Changhong Shi

Zhe Lu

Wenyong Zhang

Jian Ding

Meiyu Geng

Yi Chen

Ying Huang

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 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

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

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Shenzhen, China

Yanyan Shen