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    SYSTEM AND METHOD FOR TRAINING MACHINE LEARNING MODELS WITH UNLABELED OR WEAKLY-LABELED DATA AND APPLYING THE SAME FOR PHYSIOLOGICAL ANALYSIS
    31.
    发明申请
    SYSTEM AND METHOD FOR TRAINING MACHINE LEARNING MODELS WITH UNLABELED OR WEAKLY-LABELED DATA AND APPLYING THE SAME FOR PHYSIOLOGICAL ANALYSIS 有权

    公开(公告)号：US20220215958A1

    公开(公告)日：2022-07-07

    申请号：US17568084

    申请日：2022-01-04

    Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

    Inventor： Bin Kong ,  Youbing Yin ,  Xin Wang ,  Yi Lu ,  Haoyu Yang ,  Junjie Bai ,  Qi Song

    IPC: G16H50/20 ,  G16H30/40 ,  G06T7/00 ,  G06N20/20

    Abstract: The present disclosure relates to training methods for a machine learning model for physiological analysis. The training method may include receiving training data including a first dataset of labeled data of a physiological-related parameter and a second dataset of weakly-labeled data of the physiological-related parameter. The training method further includes training, by at least one processor, an initial machine learning model using the first dataset, and applying, by the at least one processor, the initial machine learning model to the second dataset to generate a third dataset of pseudo-labeled data of the physiological-related parameter. The training method also includes training, by the at least one processor, the machine learning model based on the first dataset and the third dataset, and providing the trained machine learning model for predicting the physiological-related parameter. Thereby, the weakly-labeled dataset may be sufficiently utilized in training of the machine learning model and improve ts p iformance.

    METHODS AND DEVICES FOR PERFORMING THREE-DIMENSIONAL BLOOD VESSEL RECONSTRUCTION USING ANGIOGRAPHIC IMAGES
    35.
    发明申请
    METHODS AND DEVICES FOR PERFORMING THREE-DIMENSIONAL BLOOD VESSEL RECONSTRUCTION USING ANGIOGRAPHIC IMAGES 审中-公开

    公开(公告)号：US20200297300A1

    公开(公告)日：2020-09-24

    申请号：US16895573

    申请日：2020-06-08

    Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

    Inventor： Qi Song ,  Youbing Yin ,  Shubao Liu ,  Xiaoxiao Liu

    IPC: A61B6/00 ,  G06T7/00

    Abstract: The disclosure provides a method and device for performing three-dimensional blood vessel reconstruction using projection images of a patient. The computer-implemented method includes receiving a first two-dimensional image of a blood vessel in a first projection direction and a three-dimensional model of the blood vessel. The method further includes determining, by a processor, a first optical path length at a selected position of the blood vessel based on the first two-dimensional image. The method also includes determining, by the processor, a second optical path length at the selected position of the blood vessel in the three-dimensional model. The method additional includes adjusting the three-dimensional model of the blood vessel, based on a comparison of the first optical path length and the second optical path length.

    AUTOMATIC METHOD AND SYSTEM FOR VESSEL REFINE SEGMENTATION IN BIOMEDICAL IMAGES USING TREE STRUCTURE BASED DEEP LEARNING MODEL
    38.
    发明申请
    AUTOMATIC METHOD AND SYSTEM FOR VESSEL REFINE SEGMENTATION IN BIOMEDICAL IMAGES USING TREE STRUCTURE BASED DEEP LEARNING MODEL 有权

    公开(公告)号：US20190325579A1

    公开(公告)日：2019-10-24

    申请号：US16392516

    申请日：2019-04-23

    Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

    Inventor： Xin Wang ,  Youbing Yin ,  Junjie Bai ,  Yi Lu ,  Qi Song

    IPC: G06T7/11 ,  G06T7/174 ,  G06T7/00 ,  G06N20/00 ,  G06N3/08

    Abstract: Embodiments of the disclosure provide systems and methods for segmenting a biomedical image including at least one tree structure object. The system includes a communication interface configured to receive the biomedical image and a learning model. The biomedical image is acquired by an image acquisition device. The system further includes at least one processor configured to extract a plurality of image patches from the biomedical image and apply the learning model to the plurality of image patches to segment the biomedical image. The learning model includes a convolutional network configured to process the plurality of image patches to construct respective feature maps and a tree structure network configured to process the feature maps collectively to obtain a segmentation mask for the tree structure object. The tree structure network models a spatial constraint of the plurality of image patches.

    Method and system for disease quantification of anatomical structures
    40.
    发明授权
    Method and system for disease quantification of anatomical structures 有权

    公开(公告)号：US12119117B2

    公开(公告)日：2024-10-15

    申请号：US17726307

    申请日：2022-04-21

    Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

    Inventor： Xin Wang ,  Youbing Yin ,  Bin Kong ,  Yi Lu ,  Hao-Yu Yang ,  Xinyu Guo ,  Qi Song

    IPC: G16H50/30 ,  G06N3/045 ,  G06T7/00 ,  G06V10/42 ,  G06V10/44 ,  G06V10/82 ,  G16H30/40

    CPC classification number: G16H50/30 ,  G06N3/045 ,  G06T7/0012 ,  G06V10/42 ,  G06V10/44 ,  G06V10/82 ,  G16H30/40

    Abstract: This disclosure discloses a method and system for predicting disease quantification parameters for an anatomical structure. The method includes extracting a centerline structure based on a medical image. The method further includes predicting the disease quantification parameter for each sampling point on the extracted centerline structure by using a GNN, with each node corresponds to a sampling point on the extracted centerline structure and each edge corresponds to a spatial constraint relationship between the sampling points. For each node, a local feature is extracted based on the image patch for the corresponding sampling point by using a local feature encoder, and a global feature is extracted by using a global feature encoder based on a set of image patches for a set of sampling points, which include the corresponding sampling point and have a spatial constraint relationship defined by the centerline structure. Then, an embed feature is obtained based on both the local feature and the global feature and input into to the node. The method is able to integrate local and global consideration factors of the sampling points into the GNN to improve the prediction accuracy.

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