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    Systems and methods for machine learning based modeling
    114.
    发明授权
    Systems and methods for machine learning based modeling 有权

    公开(公告)号：US11604984B2

    公开(公告)日：2023-03-14

    申请号：US16686539

    申请日：2019-11-18

    Applicant: SHANGHAI UNITED IMAGING INTELLIGENCE CO., LTD.

    Inventor： Abhishek Sharma ,  Arun Innanje ,  Ziyan Wu ,  Shanhui Sun ,  Terrence Chen

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A system comprising a first computing apparatus in communication with multiple second computing apparatuses. The first computing apparatus may obtain a plurality of first trained machine learning models for a task from the multiple second computing apparatuses. At least a portion of parameter values of the plurality of first trained machine learning models may be different from each other. The first computing apparatus may also obtain a plurality of training samples. The first computing apparatus may further determine, based on the plurality of training samples, a second trained machine learning model by learning from the plurality of first trained machine learning models.

    Systems and methods for image segmentation
    116.
    发明授权
    Systems and methods for image segmentation 有权

    公开(公告)号：US11488021B2

    公开(公告)日：2022-11-01

    申请号：US16905115

    申请日：2020-06-18

    Applicant: SHANGHAI UNITED IMAGING INTELLIGENCE CO., LTD.

    Inventor： Shanhui Sun ,  Pingjun Chen ,  Xiao Chen ,  Zhang Chen ,  Terrence Chen

    IPC: G06T7/10 ,  G16H30/40 ,  G06N3/08 ,  G06K9/62 ,  G06N3/04

    Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with image segmentation that may be implementing using an encoder neural network and a decoder neural network. The encoder network may be configured to receive a medical image comprising a visual representation of an anatomical structure and generate a latent representation of the medical image indicating a plurality of features of the medical image. The latent representation may be used by the decoder network to generate a mask for segmenting the anatomical structure from the medical image. The decoder network may be pre-trained to learn a shape prior associated with the anatomical structure and once trained, the decoder network may be used to constrain an output of the encoder network during training of the encoder network.

    MRI reconstruction with image domain optimization
    117.
    发明授权
    MRI reconstruction with image domain optimization 有权

    公开(公告)号：US11460528B2

    公开(公告)日：2022-10-04

    申请号：US16936571

    申请日：2020-07-23

    Applicant: Shanghai United Imaging Intelligence Co., LTD.

    Inventor： Zhang Chen ,  Shanhui Sun ,  Xiao Chen ,  Terrence Chen

    IPC: G01R33/56 ,  G06N3/08

    Abstract: An apparatus for magnetic resonance imaging (MRI) image reconstruction is provided. The apparatus accesses a training set of MRI data for training. The training set can include paired fully sampled data or unpaired fully sampled data. Undersampled MRI data is optimized in an MRI data optimization module to generate reconstructed MRI data. The apparatus builds a discriminative model using the training set and the reconstructed MRI data. During inference, the parameters of the discriminator model are fixed and the discriminator model is used to classify an output of the MRI data optimization model as the reconstructed MRI image.

    Systems and methods for reconstructing a medical image using meta learning
    118.
    发明授权
    Systems and methods for reconstructing a medical image using meta learning 有权

    公开(公告)号：US11423593B2

    公开(公告)日：2022-08-23

    申请号：US16720602

    申请日：2019-12-19

    Applicant: SHANGHAI UNITED IMAGING INTELLIGENCE CO., LTD.

    Inventor： Zhang Chen ,  Shanhui Sun ,  Terrence Chen

    IPC: G06K9/00 ,  G06T11/00 ,  G06N3/04 ,  G06N3/08 ,  G06F17/14

    Abstract: Methods and systems for reconstructing an image. For example, a method includes: receiving k-space data; receiving a transform operator corresponding to the k-space data; determining a distribution representing information associated with one or more previous iteration images; generating a next iteration image by an image reconstruction model to reduce an objective function, the objective function corresponding to a data consistency metric and a regularization metric; evaluating whether the next iteration image is satisfactory; and if the next iteration image is satisfactory, outputting the next iteration image as an output image. In certain examples, the data consistency metric corresponds to a first previous iteration image, the k-space data, and the transform operator. In certain examples, the regularization metric corresponds to the distribution. In certain examples, the computer-implemented method is performed by one or more processors.

    Abnormality detection within a defined area
    120.
    发明授权
    Abnormality detection within a defined area 有权

    公开(公告)号：US11386537B2

    公开(公告)日：2022-07-12

    申请号：US16802989

    申请日：2020-02-27

    Applicant: Shanghai United Imaging Intelligence Co., LTD.

    Inventor： Abhishek Sharma ,  Meng Zheng ,  Srikrishna Karanam ,  Ziyan Wu ,  Arun Innanje ,  Terrence Chen

    IPC: G06T7/00 ,  G16H40/67 ,  G06N20/10 ,  G06F17/18 ,  G06N3/08 ,  G06N5/04

    Abstract: Abnormality detection within a defined area includes obtaining a plurality of images of the defined area from image-capture devices. An extent of deviation of one or more types of products from an inference of each of the plurality of images is determined using a trained neural network. A localized dimensional representation is generated in a portion of an input image associated with a first location of the plurality of locations, based on gradients computed from the determined extent of deviation. The generated localized dimensional representation provides a visual indication of an abnormality located in the first location within the defined area. An action associated with the first location is executed based on the generated dimensional representation for proactive control or prevention of occurrence of undesired event in the defined area.

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