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公开(公告)号：US12087042B2
公开(公告)日：2024-09-10
申请号：US17400029
申请日：2021-08-11
Inventor： Rongbo Shen , Kezhou Yan , Kuan Tian , Cheng Jiang , Ke Zhou
IPC: G06V10/774 , G06F18/2113 , G06F18/214 , G06N3/08
CPC classification number: G06V10/7753 , G06F18/2113 , G06F18/214 , G06N3/08
Abstract: The present disclosure relates to a method for training a neural network model performed at an electronic device. The method includes: performing initial training by using a first training sample set to obtain an initial neural network model; performing a prediction on a second training sample set by using the initial neural network model to obtain a prediction result of each of training samples in the second training sample set; determining a plurality of preferred samples from the second training sample set based on the prediction results; adding the plurality of preferred samples that are annotated to the first training sample set to obtain an expanded first training sample set; updating training of the initial neural network model by using the expanded first training sample set to obtain an updated neural network model until a training ending condition is satisfied.
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公开(公告)号：US11501431B2
公开(公告)日：2022-11-15
申请号：US16905079
申请日：2020-06-18
Inventor： Fen Xiao , Jia Chang , Xuan Zhou , Ke Zhou Yan , Cheng Jiang , Kuan Tian , Jian Ping Zhu
Abstract: An image processing method performed by a terminal is provided. A molybdenum target image is obtained, and a plurality of candidate regions are extracted from the molybdenum target image. In the molybdenum target image, a target region is marked in the plurality of candidate regions by using a neural network model obtained by deep learning training, a probability that a lump comprised in the target region is a target lump being greater than a first threshold, a probability that the target lump is a malignant tumor being greater than a second threshold, and the neural network model being used for indicating a mapping relationship between a candidate region and a probability that a lump comprised in the candidate region is the target lump.
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Patent Agency Ranking
3.发明授权公开(公告)号：US12183059B2
公开(公告)日：2024-12-31
申请号：US17686950
申请日：2022-03-04
Inventor： Liang Wang , Jiarui Sun , Rongbo Shen , Cheng Jiang , Yanchun Zhu , Jianhua Yao
IPC: G06V10/26 , G06T3/4038 , G06V10/764 , G06V10/82
Abstract: An AI-based object classification method and apparatus, a computer-readable storage medium, and a computer device. The method includes: obtaining a target image to be processed, the target image including a target detection object; separating a target detection object image of the target detection object from the target image; inputting the target detection object image into a feature object prediction model to obtain a feature object segmentation image of a feature object in the target detection object image; obtaining quantitative feature information of the target detection object according to the target detection object image and the feature object segmentation image; and classifying the target detection object image according to the quantitative feature information to obtain category information of the target detection object in the target image.
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4.发明授权公开(公告)号：US11984225B2
公开(公告)日：2024-05-14
申请号：US17367280
申请日：2021-07-02
Inventor： Kuan Tian , Cheng Jiang
IPC: G06T7/62 , G06F18/213 , G06F18/22 , G06N20/00 , G06T7/00 , G06T7/11 , G06V10/10 , G06V10/25 , G06V10/26 , G06V10/82 , G06V30/19 , G06V30/262 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/70
CPC classification number: G16H50/20 , G06F18/213 , G06F18/22 , G06N20/00 , G06T7/0014 , G06T7/11 , G06T7/62 , G06V10/17 , G06V10/25 , G06V10/26 , G06V10/82 , G06V30/19147 , G06V30/19153 , G06V30/19173 , G06V30/274 , G16H30/20 , G16H30/40 , G16H50/70 , G06T2207/20021 , G06T2207/30096 , G06V2201/03
Abstract: A medical image processing method includes: obtaining a biological tissue image including a biological tissue, recognizing, in the biological tissue image, a first region of a lesion object in the biological tissue; recognizing a lesion attribute matching the lesion object; dividing an image region of the biological tissue in the biological tissue image into a plurality of quadrant regions; obtaining target quadrant position information of a quadrant region in which the first region is located; and generating medical service data according to the target quadrant position information and the lesion attribute.
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公开(公告)号：US11922654B2
公开(公告)日：2024-03-05
申请号：US17367266
申请日：2021-07-02
Inventor： Kuan Tian , Cheng Jiang , Kezhou Yan , Rongbo Shen
IPC: G06T7/70 , A61B6/12 , G06F18/24 , G06N3/08 , G06T7/00 , G06T7/73 , G16H15/00 , G16H30/20 , G16H30/40 , G16H50/20
CPC classification number: G06T7/73 , A61B6/12 , A61B6/502 , G06F18/24 , G06N3/08 , G06T7/0012 , G06T7/70 , G16H15/00 , G16H30/20 , G16H30/40 , G16H50/20 , G06T2207/10116 , G06T2207/20084 , G06T2207/30068 , G06T2207/30096 , G06V2201/03
Abstract: A computer device, obtains a mammographic image of a unilateral breast. The mammographic image includes a cranial-caudal (CC)-position mammographic image and a mediolateral-oblique (MLO)-position mammographic image. The computer device invokes a breast detection model to perform a prediction of a condition of the unilateral breast according to the CC-position mammographic image and the MLO-position mammographic image. The device obtains a prediction result of the unilateral breast, and generates and outputs a detection report that includes the prediction result.
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公开(公告)号：US11995821B2
公开(公告)日：2024-05-28
申请号：US17367316
申请日：2021-07-02
Inventor： Cheng Jiang , Kuan Tian
CPC classification number: G06T7/0012 , G06T5/73 , G06T7/11 , G06V10/25 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084 , G06T2207/30068 , G06T2207/30096
Abstract: A medical image region screening method and apparatus and a storage medium are provided. The method includes: obtaining a medical image of biological tissue, segmenting tissue regions of a plurality of tissue types from the medical image, selecting, from the tissue regions of the plurality of tissue types based on types of capturing positions of the medical image, a reserved region, obtaining a positional relationship between the reserved region and a predicted lesion region in the medical image; and screening for the predicted lesion region in the medical image based on the positional relationship, to obtain a target lesion region.
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公开(公告)号：US20200320701A1
公开(公告)日：2020-10-08
申请号：US16905079
申请日：2020-06-18
Inventor： Fen Xiao , Jia Chang , Xuan Zhou , Ke Zhou Yan , Cheng Jiang , Kuan Tian , Jian Ping Zhu
Abstract: An image processing method performed by a terminal is provided. A molybdenum target image is obtained, and a plurality of candidate regions are extracted from the molybdenum target image. In the molybdenum target image, a target region is marked in the plurality of candidate regions by using a neural network model obtained by deep learning training, a probability that a lump comprised in the target region is a target lump being greater than a first threshold, a probability that the target lump is a malignant tumor being greater than a second threshold, and the neural network model being used for indicating a mapping relationship between a candidate region and a probability that a lump comprised in the candidate region is the target lump.
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