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公开(公告)号：US12118739B2
公开(公告)日：2024-10-15
申请号：US17520715
申请日：2021-11-07
Inventor： Xinghui Fu , Han Zheng , Junwen Qiu , Hong Shang , Zhongqian Sun
CPC classification number: G06T7/60 , G06F18/24 , G06T7/0014 , G06T7/73 , A61B1/31 , G06T2207/10016 , G06T2207/10068 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004 , G06V2201/03
Abstract: A medical image processing method includes: determining a target mask of a target object in a medical image and a reference mask of a reference object in the medical image, the target mask indicating a position and a boundary of the target object, and the reference mask indicating a position and a boundary of the reference object; determining a feature size of the target object based on the target mask; determining a feature size of the reference object based on the reference mask; and determining a target size of the target object based on the feature size of the reference object, a preset mapping relationship between the feature size of the reference object and a reference size, and the feature size of the target object.
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公开(公告)号：US10713135B2
公开(公告)日：2020-07-14
申请号：US16203376
申请日：2018-11-28
Inventor： Wen Zhang , Yongfu Sun , Baiwan Zhu , Rui Li , Han Zheng , Zhigang Hao
Abstract: A data disaster tolerance method, device and system is disclosed. Each node in a logic unit including a single master node and two or more slave nodes is monitored. If the master node is abnormal, the server acquires log information of the plurality of two or more slave nodes separately, the log information of the two or more slave nodes includes respective time points of data synchronization between the slave nodes and the master node A respective slave node of the two or more slave nodes having the time point of data synchronization closest to a current time is selected as a target node. A master-slave relationship in the logic unit is updated to change a role of the target node to that of the master node.
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公开(公告)号：US12154680B2
公开(公告)日：2024-11-26
申请号：US17674126
申请日：2022-02-17
Inventor： Junwen Qiu , Zhongqian Sun , Xinghui Fu , Hong Shang , Han Zheng
Abstract: This application relates to an endoscopic image display method, apparatus, computer device, and storage medium, and relates to the field of machine learning technologies. The method acquiring an endoscopic image; locating a target region image in the endoscopic image, the target region image being a partial image comprising a target region; inputting the target region image into a coding network to obtain a semantic feature of the target region image, the coding network being a part of an image classification network, and the image classification network being a machine learning network obtained through training with first training images; matching the semantic feature of the target region image against semantic features of image samples to obtain a matching result, the matching result indicating a target image sample that matches the target region image; and displaying the endoscopic image and the matching result in an endoscopic image display interface.
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Patent Agency Ranking
公开(公告)号：US11967414B2
公开(公告)日：2024-04-23
申请号：US17321219
申请日：2021-05-14
Inventor： Han Zheng , Zhongqian Sun , Hong Shang , Xinghui Fu , Wei Yang
CPC classification number: G16H30/40 , G06T7/0012 , G16H50/20 , G06T2207/10068 , G06T2207/20081 , G06T2207/20084 , G06T2207/30096
Abstract: This application relates to an image recognition model training method, an image recognition method, apparatus, and system. The method includes: obtaining a to-be-recognized image; extracting image feature information of the to-be-recognized image; and obtaining a lesion category recognition result of the to-be-recognized image by using the image feature information of the to-be-recognized image as an input parameter of a preset image recognition model, the image recognition model being trained by using a training image sample set comprising at least one strong-label training image sample, to determine the lesion category recognition result; and the strong-label training image sample representing an image sample having strong-label information, and the strong-label information comprising at least annotation information of a lesion category and a lesion position in the strong-label training image sample. According to the lesion position, image feature information of a specific lesion category may be more accurately positioned, thereby improving reliability and accuracy.
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5.发明授权公开(公告)号：US12198407B2
公开(公告)日：2025-01-14
申请号：US17710254
申请日：2022-03-31
Inventor： Han Zheng , Hong Shang , Xiaoning Wang , Jianhua Yao
IPC: G06V10/77 , G06V10/74 , G06V10/764 , G06V10/774 , G06V10/776 , G06V10/82
Abstract: An artificial intelligence-based image processing method includes: obtaining a first sample image of a source domain and a second sample image of a target domain, the first sample image of the source domain carrying a corresponding target processing result; converting the first sample image into a target sample image, the target sample image carrying a corresponding target processing result; training a first image processing model based on the target sample image and the target processing result corresponding to the target sample image, to obtain a second image processing model; and inputting, in response to obtaining a human tissue image of the target domain, the human tissue image into the second image processing model, positioning, by the second image processing model, a target human tissue in the human tissue image, and outputting position information of the target human tissue in the human tissue image.
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公开(公告)号：US12242570B2
公开(公告)日：2025-03-04
申请号：US18438595
申请日：2024-02-12
Inventor： Hong Shang , Han Zheng , Zhongqian Sun
Abstract: A method for training an image recognition model includes: obtaining training image sets; obtaining a first predicted probability, a second predicted probability, a third predicted probability, and a fourth predicted probability based on the training image sets by using an initial image recognition model; determining a target loss function according to the first predicted probability, the second predicted probability, the third predicted probability, and the fourth predicted probability; and training the initial image recognition model based on the target loss function, to obtain an image recognition model.
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7.发明授权公开(公告)号：US12051199B2
公开(公告)日：2024-07-30
申请号：US17515174
申请日：2021-10-29
Inventor： Lishu Luo , Hong Shang , Zhongqian Sun , Han Zheng
IPC: G06K9/00 , G06F18/214 , G06N3/04 , G06T7/00 , G06V10/25
CPC classification number: G06T7/0012 , G06F18/214 , G06N3/04 , G06V10/25 , G06T2207/10068 , G06T2207/30096
Abstract: Embodiments of this application disclose an image processing method performed by a computer device, and a computer-readable storage medium. The method includes: obtaining a to-be-detected image, and performing down-sampling abnormality classification processing on the to-be-detected image, to obtain a predicted abnormality category label and a target feature image; performing preliminary abnormality positioning processing based on the predicted abnormality category label and the target feature image, to obtain an initial positioning image corresponding to the to-be-detected image; performing up-sampling abnormality positioning processing on the initial positioning image, to obtain a target positioning image corresponding to the to-be-detected image; and outputting the predicted abnormality category label and the target positioning image, the initial positioning image and the target positioning image being configured for reflecting attribute information of a target region associated with the predicted abnormality category label within the to-be-detected image.
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公开(公告)号：US11960571B2
公开(公告)日：2024-04-16
申请号：US17515312
申请日：2021-10-29
Inventor： Hong Shang , Han Zheng , Zhongqian Sun
CPC classification number: G06F18/2155 , G06F18/217 , G06N7/01 , G16H30/40 , G16H50/20 , G06V2201/03
Abstract: A method for training an image recognition model includes: obtaining training image sets; obtaining a first predicted probability, a second predicted probability, a third predicted probability, and a fourth predicted probability based on the training image sets by using an initial image recognition model; determining a target loss function according to the first predicted probability, the second predicted probability, the third predicted probability, and the fourth predicted probability; and training the initial image recognition model based on the target loss function, to obtain an image recognition model.
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