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1.发明申请公开(公告)号：US20220036550A1
公开(公告)日：2022-02-03
申请号：US17503160
申请日：2021-10-15
Inventor： Fubo ZHANG , Dong WEI , Kai MA , Yefeng ZHENG
Abstract: The present disclosure provides an artificial intelligence-based (AI-based) medical image processing method performed by a computing device, and a non-transitory computer-readable storage medium. The AI-based medical image processing method includes: processing a medical image to generate an encoded intermediate image; processing the encoded intermediate image, to segment a first feature and generate a segmented intermediate image; processing the encoded intermediate image and the segmented intermediate image based on an attention mechanism, to generate a detected intermediate input image; and performing second feature detection on the detected intermediate input image, to determine whether an image region of the detected intermediate input image in which the first feature is located comprises a second feature.
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公开(公告)号：US20210233247A1
公开(公告)日：2021-07-29
申请号：US17229707
申请日：2021-04-13
Inventor： Shilei CAO , Kai MA , Yefeng ZHENG
Abstract: This application relates to a medical image segmentation method, a computer device, and a storage medium. The method includes: obtaining medical image data; obtaining a target object and weakly supervised annotation information of the target object in the medical image data; determining a pseudo segmentation mask for the target object in the medical image data according to the weakly supervised annotation information; and performing mapping on the medical image data by using a preset mapping model based on the pseudo segmentation mask, to obtain a target segmentation result for the target object. Because the medical image data is segmented based on the weakly supervised annotation information, there is no need to annotate information by using much labor during training of the preset mapping model, thereby saving labor costs. The preset mapping model is a model used for mapping the medical image data based on the pseudo segmentation mask.
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3.发明公开公开(公告)号：US20230343063A1
公开(公告)日：2023-10-26
申请号：US18216918
申请日：2023-06-30
Inventor： Zhe XU , Donghuan LU , Kai MA , Yefeng ZHENG
IPC: G06V10/26 , G06V20/70 , G06V10/82 , G06V10/77 , G06V10/74 , G06T7/194 , G06V10/776 , G06V10/774
CPC classification number: G06V10/267 , G06V20/70 , G06V10/82 , G06V10/7715 , G06V10/761 , G06T7/194 , G06V10/776 , G06V10/774 , G06T2207/20084 , G06T2207/20076 , G06T2207/20081 , G06V2201/03 , G06T2207/30096 , G06T2207/30016 , G06T2207/30084
Abstract: An image segmentation model training method includes acquiring a first image, a second image, and a labeled image of the first image; acquiring a first predicted image according to a first network model; acquiring a second predicted image according to a second network model; determining a reference image of the second image based on the second image and the labeled image of the first image; and updating a model parameter of the first network model based on the first predicted image, the labeled image, the second predicted image, and the reference image to obtain an image segmentation model.
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公开(公告)号：US20230092619A1
公开(公告)日：2023-03-23
申请号：US18072337
申请日：2022-11-30
Inventor： Yuexiang LI , Nanjun HE , Kai MA , Yefeng ZHENG
IPC: G06V10/764 , G06V10/774
Abstract: An image classification method includes: performing image segmentation on an unlabeled sample image to obtain image blocks and performing feature extraction on each image block to obtain an initial image feature set including an initial image feature corresponding to each image block, rearranging and combining initial image features in the initial image feature set to obtain a first image feature set and a second image feature set, first image features in the first image feature set and second image features in the second image feature set corresponding to different rearrangement and combination manners, pre-training an image classification model based on the first image feature set and the second image feature set, the image classification model being configured to classify content in an image, and fine-tuning the pre-trained image classification model based on a labeled sample image.
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公开(公告)号：US20230077726A1
公开(公告)日：2023-03-16
申请号：US17992565
申请日：2022-11-22
Inventor： Luyan LIU , Kai MA , Yefeng ZHENG
IPC: A61B5/00
Abstract: A method for classification processing of an electrophysiological signal, including acquiring an electrophysiological signal collected by an acquisition device, and acquiring a channel association feature corresponding to the acquisition device. The channel association feature indicates spatial locations of multiple acquisition channels of the acquisition device, each of the multiple acquisition channels collecting the electrophysiological signal at a respective spatial location. The method further includes extracting a time feature corresponding to the electrophysiological signal, and generating an embedded feature based on the channel association feature and the time feature, and extracting a spatial feature corresponding to the embedded feature, and obtaining a classification result corresponding to the electrophysiological signal based on the spatial feature.
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Patent Agency Ranking
公开(公告)号：US20230074520A1
公开(公告)日：2023-03-09
申请号：US17985785
申请日：2022-11-11
Inventor： Dong WEI , Donghuan LU , Hong LIU , Yuexiang LI , Kai MA , Yefeng ZHENG , Liansheng WANG
Abstract: A computer device performs feature extraction on two-dimensional medical images included in a three-dimensional medical image, to obtain image features corresponding to the two-dimensional medical images. The three-dimensional medical image are obtained by continuously scanning a target tissue structure. The computer device determines offsets of the two-dimensional medical images in a target direction based on the image features. The computer device performs feature alignment on the image features based on the offsets, to obtain aligned image features. The computer device performs three-dimensional segmentation on the three-dimensional medical image based on the aligned image features, to obtain three-dimensional layer distribution of the target tissue structure in the three-dimensional medical image.
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公开(公告)号：US20220148191A1
公开(公告)日：2022-05-12
申请号：US17587825
申请日：2022-01-28
Inventor： Luyan LIU , Kai MA , Yefeng ZHENG
Abstract: An image segmentation method includes obtaining target domain images and source domain images, and segmenting the source domain images and the target domain images by using a generative network in a first generative adversarial network. The method further includes segmenting the source domain images and the target domain images by using a generative network in a second generative adversarial network, and determining a first source domain image and a second source domain image according to source domain segmentation losses, and determining a first target domain image and a second target domain image according to target domain segmentation losses. The method also includes performing cross training on the first generative adversarial network and the second generative adversarial network to obtain a trained first generative adversarial network; and segmenting a to-be-segmented image based on the generative network in the trained first generative adversarial network.
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公开(公告)号：US20220036187A1
公开(公告)日：2022-02-03
申请号：US17502847
申请日：2021-10-15
Inventor： Dong WEI , Kai MA , Yefeng ZHENG
Abstract: A sample generation method outputs a dummy sample set generated by a trained sample generation network that operates on spliced vectors formed by combining real category feature vectors extracted from real samples with real category label vectors corresponding to the real samples. The trained sample generation network is trained using real samples and dummy samples that are generated by an intermediate sample generation network operating on the spliced vectors. The training includes inputting the real samples and the dummy samples to an intermediate sample discrimination network, performing iterative adversarial training of the intermediate sample generation network and the intermediate sample discrimination network until an iteration stop condition is met. As a result, the dummy sample set output by the trained sample generation network includes dummy samples that are not easily differentiated from real samples and that are already labeled with category information, for accurate use in training classifiers.
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9.发明申请公开(公告)号：US20210251590A1
公开(公告)日：2021-08-19
申请号：US17246339
申请日：2021-04-30
Inventor： Heng GUO , Xingde YING , Kai MA , Yefeng ZHENG
Abstract: This disclosure discloses a CT image generation method and apparatus, a computer device, and a computer-readable storage medium. The method includes: obtaining a first X-ray image and a second X-ray image, the first X-ray image and the second X-ray image being X-ray images acquired for a target object from two orthogonal viewing angles; calling a generator to perform three-dimensional reconstruction on the first X-ray image and the second X-ray image, to obtain a three-dimensional model of the target object; and obtaining a CT image of the target object according to the three-dimensional model of the target object.
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公开(公告)号：US20180352415A1
公开(公告)日：2018-12-06
申请号：US16056098
申请日：2018-08-06
Inventor： Kai MA , Maohua CHEN , Mo ZHAO , Zhenxi QIU , Xiaoming WU , Nan CHENG , Xiaohui ZHENG , Junxiong CHEN , Jinheng XIE , Zhe CHENG , Le YU , Shuhui MEI , Chi ZHANG , Huiqin YANG , Yao QIN , Shunfu YE , Tao ZHANG , Wenrong TANG , Yangbin HUANG , Ming HE , Chaoxiong DIAO , Pengbo ZHANG , Guanqiao SU , Hongmin ZHENG , Xiaojuan ZHANG , Zhejin HUANG , Xiaoyang QIAN , Zhongming GUO , Xiaoyi FANG , Yang ZUO , Yan DAI
CPC classification number: H04W8/005 , G06Q50/01 , H04B17/318 , H04W4/029 , H04W4/12 , H04W4/21 , H04W4/80 , H04W8/18 , H04W64/00 , H04W84/12
Abstract: Embodiments of this application provide a near-field wireless communication service processing method performed at a first computing device. While running a social networking application, the first computing device listens to a near-field wireless communication signal broadcasted by a second computing device. After detecting the near-field wireless communication signal broadcasted by the second computing device, first computing device processes the near-field wireless communication signal to obtain identification information associated with the second computing device. The first computing device sends the identification information associated with the second computing device to a remote server supporting the social networking application and obtains a preset service page corresponding to the identification information associated with the second computing device from the server, and displays the preset service page within the social networking application on the first computing device.
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