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11.发明申请公开(公告)号：US20230080098A1
公开(公告)日：2023-03-16
申请号：US17991385
申请日：2022-11-21
Inventor： Nanjun HE , Donghuan LU , Yuexiang LI , Yi LIN , Kai MA , Yefeng ZHENG
Abstract: An object recognition method includes extracting, by a first Transformer network, spatial features of a plurality of medical images respectively, the plurality of medical images being images of a same object at different times, and fusing the extracted plurality of spatial features, to obtain a first fusion spatial feature of the object. The method further includes extracting, by a second Transformer network, a spatial-temporal feature of the object based on the first fusion spatial feature. The spatial-temporal feature indicates a change in the spatial features of the plurality of medical images at the different times. The method further includes recognizing a state of the object based on the spatial-temporal feature, to obtain a recognition result of the object.
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12.发明申请公开(公告)号：US20230051411A1
公开(公告)日：2023-02-16
申请号：US17976971
申请日：2022-10-31
Inventor： Jiawei CHEN , Yuexiang LI , Kai MA , Yefeng ZHENG
IPC: G06V10/774 , G06V10/74 , G06V10/82
Abstract: Methods and apparatuses for image processing are provided. A first image belonging to a first image domain is acquired and input to an image processing model to be trained to obtain a second image belonging to a second image domain. A first correlation degree between an image feature of the first image and an image feature of the second image to obtain a target feature correlation degree is calculated. A second correlation degree between feature value distribution of the image feature of the first image and feature value distribution of the image feature of the second image is calculated to obtain a distribution correlation degree. Model parameters of an image processing model are adjusted to a direction in which the target feature correlation degree is increased and a direction in which the distribution correlation degree is increased to obtain a trained image processing.
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13.发明申请公开(公告)号：US20220147151A1
公开(公告)日：2022-05-12
申请号：US17580545
申请日：2022-01-20
Inventor： Xiaolin HONG , Qingqing ZHENG , Xinmin WANG , Kai MA , Yefeng ZHENG
Abstract: This application provides a gesture information processing method and apparatus, an electronic device, and a storage medium. The method includes: determining an electromyography signal collection target object in a gesture information usage environment; dividing the electromyography signal sample through a sliding window having a fixed window value and a fixed stride into different electromyography signals of the target object, and denoising the different electromyography signals of the target object; recognizing the denoised different electromyography signals, and determining probabilities of gesture information represented by the different electromyography signals; and weighting the probabilities of the gesture information represented by the different electromyography signals, so as to determine gesture information matching the target object.
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14.发明申请公开(公告)号：US20220036135A1
公开(公告)日：2022-02-03
申请号：US17501899
申请日：2021-10-14
Inventor： Yifan HU , Yuexiang LI , Yefeng ZHENG
Abstract: A method for determining a target image to be labeled includes: obtaining an original image and an autoencoder (AE) set, the original image being an image having not been labeled, the AE set including N AEs; obtaining an encoded image set corresponding to the original image by using the AE set, the encoded image set including N encoded images, the encoded images being corresponding to the AEs; obtaining the encoded image set and a segmentation result set corresponding to the original image by using an image segmentation network, the image segmentation network including M image segmentation sub-networks, and the segmentation result set including [(N+1)*M] segmentation results; determining labeling uncertainty corresponding to the original image according to the segmentation result set; and determining whether the original image is a target image according to the labeling uncertainty.
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公开(公告)号：US20210248751A1
公开(公告)日：2021-08-12
申请号：US17241800
申请日：2021-04-27
Inventor： Heng GUO , Yuexiang LI , Yefeng ZHENG
Abstract: Embodiments of this application disclose a brain image segmentation method and apparatus, a network device, and a storage medium. The method includes obtaining, by a device, a to-be-segmented image group comprising a plurality of modal images of a brain. The device includes a memory storing instructions and a processor in communication with the memory. The method further includes performing, by the device, skull stripping according to the plurality of modal images to obtain a skull-stripped mask; separately performing, by the device, feature extraction on the plurality of modal images to obtain extracted features, and fusing the extracted features to obtain a fused feature; segmenting, by the device, encephalic tissues according to the fused feature to obtain an initial segmentation result; and fusing, by the device, the skull-stripped mask and the initial segmentation result to obtain a segmentation result corresponding to the to-be-segmented image group.
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Patent Agency Ranking
公开(公告)号：US20230101539A1
公开(公告)日：2023-03-30
申请号：US18076098
申请日：2022-12-06
Inventor： Luyan LIU , Kai MA , Yefeng ZHENG
IPC: A61B5/00 , A61B5/117 , G06F18/2413
Abstract: A physiological electric signal classification processing method includes: performing data alignment on an initial physiological electric signal corresponding to a target user identity based on target signal spatial information corresponding to the target user identify to obtain a target physiological electric signal; performing spatial feature extraction on the target physiological electric signal based on a target spatial filtering matrix to obtain a target spatial feature, the target spatial filtering matrix being generated based on target training physiological electric signals corresponding to a plurality of training user identities respectively and training labels corresponding to the target training physiological electric signals, the target training physiological electric signals being obtained by performing data alignment on initial training physiological electric signals based on training signal spatial information corresponding to the training user identities; and obtaining a classification result corresponding to the initial physiological electric signal based on the target spatial feature.
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17.发明申请公开(公告)号：US20230035366A1
公开(公告)日：2023-02-02
申请号：US17964739
申请日：2022-10-12
Inventor： Donghuan LU , Junjie ZHAO , Kai MA , Yefeng ZHENG
IPC: G06V10/774 , G06V10/764 , G06V10/32
Abstract: An image classification model training method and apparatus are provided. Classification results of each image outputted by an image classification model are obtained. When the classification results outputted by the image classification model do not meet a reference condition, a reference classification result is constructed based on the classification results outputted by the image classification model. Because the reference classification result can indicate a probability that images belong to each class, a parameter of the image classification model is updated to obtain a trained image classification model based on a total error value between the classification results of the each image and the reference classification result.
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公开(公告)号：US20230021551A1
公开(公告)日：2023-01-26
申请号：US17955726
申请日：2022-09-29
Inventor： Donghuan LU , Kai MA , Yefeng ZHENG
IPC: G06V10/774 , G06T7/149 , G06V10/40 , G06V10/764 , G06T3/40
Abstract: A method for training an image segmentation model includes calling an encoder to perform feature extraction on a sample image and a scale image to obtain a sample image feature and a scale image feature. The method also includes performing class activation graph calculation to obtain a sample class activation graph and a scale class activation graph. The method also includes calling a decoder to obtain a sample segmentation result of the sample image, and calling the decoder to obtain a scale segmentation result of the scale image. The method also includes calculating a class activation graph loss and calculating a scale loss. The method also includes training the decoder based on the class activation graph loss and the scale loss.
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公开(公告)号：US20220233160A1
公开(公告)日：2022-07-28
申请号：US17721806
申请日：2022-04-15
Inventor： Shilei CAO , Hualuo LIU , Yefeng ZHENG
Abstract: This application relates to an image data inspection method and apparatus in the field of artificial intelligence (AI) technologies. The method includes obtaining an image to be inspected, the image to be inspected comprising a sequence of slice images; determining a corresponding group of slice images for each target image in the sequence of slice images; extracting a corresponding slice feature map for each slice image in the group of slice images; aligning the slice feature maps extracted corresponding to the group of slice images; aggregating context information of each slice image in the group of slice images by using an aligned feature map; and performing target region inspection on an aggregated feature map, to obtain an inspection result corresponding to the target image, and combining the inspection result corresponding to each target image, to generate an inspection result corresponding to the image to be inspected.
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20.发明申请公开(公告)号：US20220215558A1
公开(公告)日：2022-07-07
申请号：US17703829
申请日：2022-03-24
Inventor： Luyan LIU , Kai MA , Yefeng ZHENG
Abstract: A method for three-dimensional edge detection includes obtaining, for each of plural two-dimensional slices of a three-dimensional image, a two-dimensional object detection result and a two-dimensional edge detection result, stacking the two-dimensional object detection results into a three-dimensional object detection result, and stacking the two-dimensional edge detection results into a three-dimensional edge detection result. The method also includes performing encoding according to a feature map of the three-dimensional image, the three-dimensional object detection result, and the three-dimensional edge detection result, to obtain an encoding result, and performing decoding according to the encoding result, the three-dimensional object detection result, and the three-dimensional edge detection result, to obtain an optimized three-dimensional edge detection result of the three-dimensional image.
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