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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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2.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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