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    ARTIFICIAL INTELLIGENCE-BASED PATHOLOGICAL IMAGE PROCESSING METHOD AND APPARATUS, ELECTRONIC DEVICE, AND STORAGE MEDIUM
    1.
    发明申请
    ARTIFICIAL INTELLIGENCE-BASED PATHOLOGICAL IMAGE PROCESSING METHOD AND APPARATUS, ELECTRONIC DEVICE, AND STORAGE MEDIUM 有权

    公开(公告)号：US20230005156A1

    公开(公告)日：2023-01-05

    申请号：US17903851

    申请日：2022-09-06

    Applicant: Tencent Technology (Shenzhen) Company Limited

    Inventor： Liang WANG ,  Kaiwen XIAO ,  Kuan TIAN ,  Jianhua YAO

    IPC: G06T7/11 ,  G06T7/00

    Abstract: This application provides an artificial intelligence-based pathological image processing method performed by an electronic device. The method includes: determining a seed pixel of an immune cell region from a pathological image; obtaining a seed pixel mask image corresponding to the seed pixel of the immune cell region from the pathological image based on the seed pixel of the immune cell region; segmenting an epithelial cell region in the pathological image, to obtain an epithelial cell mask image of the pathological image; fusing the seed pixel mask image and the epithelial cell mask image of the pathological image, to obtain an effective seed pixel mask image corresponding to the immune cell region in the pathological image; and determining a ratio value of the immune cell region in the pathological image based on the effective seed pixel mask image.

    METHOD, APPARATUS, AND ELECTRONIC DEVICE FOR TRAINING NEURAL NETWORK MODEL
    2.
    发明申请
    METHOD, APPARATUS, AND ELECTRONIC DEVICE FOR TRAINING NEURAL NETWORK MODEL 有权

    公开(公告)号：US20210374474A1

    公开(公告)日：2021-12-02

    申请号：US17400029

    申请日：2021-08-11

    Applicant: Tencent Technology (Shenzhen) Company Limited

    Inventor： Rongbo SHEN ,  Kezhou YAN ,  Kuan TIAN ,  Cheng JIANG ,  Ke ZHOU

    IPC: G06K9/62 ,  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.

    IMAGE PROCESSING METHOD AND APPARATUS
    4.
    发明申请
    IMAGE PROCESSING METHOD AND APPARATUS 有权

    公开(公告)号：US20220207744A1

    公开(公告)日：2022-06-30

    申请号：US17697689

    申请日：2022-03-17

    Applicant: Tencent Technology (Shenzhen) Company Limited

    Inventor： Kuan TIAN ,  Jun ZHANG

    IPC: G06T7/11 ,  G06T7/194

    Abstract: The disclosure provides an image processing method and apparatus. The method may include acquiring a target image having at least two division regions. The division region includes an annotation point, and the annotation point is corresponding to a target segmentation object. The method may further include performing iterative expansion on the annotation points to obtain an iterative expansion region. The method may further include determining a first loss result between the iterative expansion region and the division region and a second loss result between the iterative expansion region and the annotation point. The method may further include, when the first loss result and the second loss result meeting a termination condition, stopping the iterative expansion to obtain a resulting iterative expansion region. The method may further include determining the resulting iterative expansion region as a region where the target segmentation object is located in the target image.

    VIDEO COMPRESSION METHOD, VIDEO DECODING METHOD, AND RELATED APPARATUSES
    8.
    发明申请
    VIDEO COMPRESSION METHOD, VIDEO DECODING METHOD, AND RELATED APPARATUSES 有权

    公开(公告)号：US20250056001A1

    公开(公告)日：2025-02-13

    申请号：US18931813

    申请日：2024-10-30

    Applicant: Tencent Technology (Shenzhen) Company Limited

    Inventor： Feng LUO ,  Jinxi XIANG ,  Kuan TIAN ,  Jun ZHANG

    IPC: H04N19/137 ,  H04N19/172 ,  H04N19/182 ,  H04N19/196 ,  H04N19/42 ,  H04N19/60 ,  H04N19/91

    Abstract: This application discloses a video compression method, a video decoding method, and related apparatuses. The method includes: extracting a key point from a to-be-processed video frame and a previous video frame respectively to obtain first position information and second position information; performing motion estimation based on the first position information and the second position information to obtain motion information; performing image inpainting based on the motion information and the previous video frame to obtain an initial video frame; determining a latent feature based on the to-be-processed video frame and the initial video frame; and performing video compression based on the first position information, the second position information, and the latent feature to obtain a video compressed file.

    TRAINING METHOD AND APPARATUS FOR IMAGE REGION SEGMENTATION MODEL, AND IMAGE REGION SEGMENTATION METHOD AND APPARATUS
    9.
    发明申请
    TRAINING METHOD AND APPARATUS FOR IMAGE REGION SEGMENTATION MODEL, AND IMAGE REGION SEGMENTATION METHOD AND APPARATUS 有权

    公开(公告)号：US20220222932A1

    公开(公告)日：2022-07-14

    申请号：US17707045

    申请日：2022-03-29

    Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

    Inventor： Jun ZHANG ,  Kuan TIAN ,  Kezhou YAN ,  Jianhua YAO ,  Xiao HAN

    IPC: G06V10/774 ,  G06V10/82 ,  G06T7/11 ,  G06T7/194

    Abstract: Embodiments of this application disclose a method and apparatus for training an image region segmentation model, and an image region segmentation method and apparatus. The method includes acquiring a sample image set, and each image of the sample image set having first annotation information; generating graph structure data corresponding to a sample image in the sample image set, the graph structure data comprising multiple nodes, and each node comprising at least one pixel in the sample image; determining second annotation information of each node according to the graph structure data and the first annotation information corresponding to the sample image by using a graph convolutional network model, a granularity of the second annotation information being smaller than a granularity of the first annotation information, the graph convolutional network model being a part of an image segmentation model; and training the image segmentation model according to the second annotation information.

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