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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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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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