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    DEEP LEARNING MEDICAL SYSTEMS AND METHODS FOR IMAGE ACQUISITION
    21.
    发明申请
    DEEP LEARNING MEDICAL SYSTEMS AND METHODS FOR IMAGE ACQUISITION 审中-公开

    公开(公告)号：US20180144466A1

    公开(公告)日：2018-05-24

    申请号：US15360626

    申请日：2016-11-23

    Applicant: General Electric Company

    Inventor： Jiang Hsieh ,  Gopal Avinash ,  Saad Sirohey

    IPC: G06T7/00 ,  G06N3/04 ,  G06N3/08

    CPC classification number: G06T7/0012 ,  G06F19/00 ,  G06N3/04 ,  G06N3/08 ,  G06T2207/10004 ,  G06T2207/20081 ,  G06T2207/20084 ,  G16H30/40 ,  G16H40/40

    Abstract: Methods and apparatus for improved deep learning for image acquisition are provided. An imaging system configuration apparatus includes a training learning device including a first processor to implement a first deep learning network (DLN) to learn a first set of imaging system configuration parameters based on a first set of inputs from a plurality of prior image acquisitions to configure at least one imaging system for image acquisition, the training learning device to receive and process feedback including operational data from the plurality of image acquisitions by the at least one imaging system. The example apparatus includes a deployed learning device including a second processor to implement a second DLN, the second DLN generated from the first DLN of the training learning device, the deployed learning device configured to provide a second imaging system configuration parameter to the imaging system in response to receiving a second input for image acquisition.

    CLASSIFICATION BASED ON ANNOTATION INFORMATION
    23.
    发明申请
    CLASSIFICATION BASED ON ANNOTATION INFORMATION 审中-公开

    公开(公告)号：US20200012884A1

    公开(公告)日：2020-01-09

    申请号：US16054373

    申请日：2018-08-03

    Applicant: General Electric Company

    Inventor： Qian Zhao ,  Min Zhang ,  Gopal Avinash

    IPC: G06K9/62 ,  G06N3/08 ,  G06K9/46 ,  G06K9/66

    Abstract: Systems and techniques for classification based on annotation information are presented. In one example, a system trains a convolutional neural network based on training data and a plurality of images. The training data is associated with a plurality of patients from at least one imaging device. The plurality of images is associated with a plurality of masks from a plurality of objects. The system also generates a first loss function based on the plurality of masks, a second loss function based on a plurality of image level labels associated with the plurality of images, and a third loss function based on the first loss function and the second loss function, where the third loss function is iteratively back propagated to tune parameters of the convolutional neural network. The system also predicts a classification label for an input image based on the convolutional neural network.

    DEEP LEARNING MEDICAL SYSTEMS AND METHODS FOR MEDICAL PROCEDURES
    24.
    发明申请
    DEEP LEARNING MEDICAL SYSTEMS AND METHODS FOR MEDICAL PROCEDURES 审中-公开

    公开(公告)号：US20190220975A1

    公开(公告)日：2019-07-18

    申请号：US16359647

    申请日：2019-03-20

    Applicant: General Electric Company

    Inventor： Jiang Hsieh ,  Gopal Avinash ,  Saad Sirohey

    IPC: G06T7/00 ,  G06N3/08 ,  G16H30/40 ,  G16H40/40 ,  G06N3/04

    CPC classification number: G06T7/0012 ,  G06N3/04 ,  G06N3/08 ,  G06T2207/10004 ,  G06T2207/20081 ,  G06T2207/20084 ,  G16H30/40 ,  G16H40/40

    Abstract: Methods and apparatus for monitoring and improving imaging system operation are provided. An example apparatus includes a first deployed deep learning network (DLN) which operates with an acquisition engine to generate an imaging device configuration. The example apparatus includes a second deployed DLN which operates with a reconstruction engine based on acquired image data. The example apparatus includes a first assessment engine with a third deployed DLN. The assessment engine receives output from at least one of the acquisition engine or the reconstruction engine to assess operation of the respective at least one of the acquisition engine or the reconstruction engine and to provide feedback to the respective at least one of the acquisition engine or the reconstruction engine. The first deployed DLN and the second deployed DLN are generated and deployed from first and second training DLNS, respectively.

    Deep learning medical systems and methods for medical procedures
    25.
    发明授权
    Deep learning medical systems and methods for medical procedures 有权

    公开(公告)号：US10242443B2

    公开(公告)日：2019-03-26

    申请号：US15360410

    申请日：2016-11-23

    Applicant: General Electric Company

    Inventor： Jiang Hsieh ,  Gopal Avinash ,  Saad Sirohey

    IPC: G06T7/00 ,  G06N3/04 ,  G06N3/08 ,  G06F19/00 ,  G16H30/40 ,  G16H40/40

    Abstract: Methods and apparatus for monitoring and improving imaging system operation are provided. An example apparatus includes a first deployed deep learning network (DLN) which operates with an acquisition engine to generate an imaging device configuration. The example apparatus includes a second deployed DLN which operates with a reconstruction engine based on acquired image data. The example apparatus includes a first assessment engine with a third deployed DLN. The assessment engine receives output from at least one of the acquisition engine or the reconstruction engine to assess operation of the respective at least one of the acquisition engine or the reconstruction engine and to provide feedback to the respective at least one of the acquisition engine or the reconstruction engine. The first deployed DLN and the second deployed DLN are generated and deployed from first and second training DLNS, respectively.

    Deep learning medical systems and methods for image acquisition
    26.
    发明授权
    Deep learning medical systems and methods for image acquisition 有权

    公开(公告)号：US10127659B2

    公开(公告)日：2018-11-13

    申请号：US15360626

    申请日：2016-11-23

    Applicant: General Electric Company

    Inventor： Jiang Hsieh ,  Gopal Avinash ,  Saad Sirohey

    IPC: G06K9/00 ,  G06T7/00 ,  G06N3/04 ,  G06N3/08

    Abstract: Methods and apparatus for improved deep learning for image acquisition are provided. An imaging system configuration apparatus includes a training learning device including a first processor to implement a first deep learning network (DLN) to learn a first set of imaging system configuration parameters based on a first set of inputs from a plurality of prior image acquisitions to configure at least one imaging system for image acquisition, the training learning device to receive and process feedback including operational data from the plurality of image acquisitions by the at least one imaging system. The example apparatus includes a deployed learning device including a second processor to implement a second DLN, the second DLN generated from the first DLN of the training learning device, the deployed learning device configured to provide a second imaging system configuration parameter to the imaging system in response to receiving a second input for image acquisition.

    Hardware system design improvement using deep learning algorithms
    27.
    发明授权
    Hardware system design improvement using deep learning algorithms 有权

    公开(公告)号：US11003988B2

    公开(公告)日：2021-05-11

    申请号：US15360042

    申请日：2016-11-23

    Applicant: General Electric Company

    Inventor： Jiang Hsieh ,  Gopal Avinash ,  Saad Sirohey

    IPC: G06N3/08 ,  G06F11/30 ,  G06N3/04 ,  G06F30/20 ,  G06F111/10

    Abstract: Methods and apparatus for deep learning-based system design improvement are provided. An example system design engine apparatus includes a deep learning network (DLN) model associated with each component of a target system to be emulated, each DLN model to be trained using known input and known output, wherein the known input and known output simulate input and output of the associated component of the target system, and wherein each DLN model is connected as each associated component to be emulated is connected in the target system to form a digital model of the target system. The example apparatus also includes a model processor to simulate behavior of the target system and/or each component of the target system to be emulated using the digital model to generate a recommendation regarding a configuration of a component of the target system and/or a structure of the component of the target system.

    Systems and methods to determine disease progression from artificial intelligence detection output
    28.
    发明授权
    Systems and methods to determine disease progression from artificial intelligence detection output 有权

    公开(公告)号：US10811135B2

    公开(公告)日：2020-10-20

    申请号：US16233670

    申请日：2018-12-27

    Applicant: General Electric Company

    Inventor： Katelyn Nye ,  Gireesha Rao ,  Gopal Avinash ,  Christopher Austin

    IPC: G06T7/00 ,  G16H30/40 ,  G06T7/11 ,  G16H50/20

    Abstract: Apparatus, systems, and methods to improve automated identification, monitoring, processing, and control of a condition impacting a patient using image data and artificial intelligence classification are disclosed. An example image processing apparatus includes an artificial intelligence classifier to: process first image data for a patient from a first time to determine a first classification result indicating a first severity of a condition for the patient; and process second image data for the patient from a second time to determine a second classification result indicating a second severity of the condition for the patient. The example image processing apparatus includes a comparator to compare the first classification result and the second classification result to determine a change and a progression of the condition associated with the change. The example image processing apparatus includes an output generator to trigger an action when the progression corresponds to a worsening of the condition.

    Classification and localization based on annotation information
    29.
    发明授权
    Classification and localization based on annotation information 有权

    公开(公告)号：US10755147B2

    公开(公告)日：2020-08-25

    申请号：US16046084

    申请日：2018-07-26

    Applicant: General Electric Company

    Inventor： Qian Zhao ,  Min Zhang ,  Gopal Avinash

    IPC: G06K9/62

    Abstract: Systems and techniques for classification and localization based on annotation information are presented. In one example, a system trains a convolutional neural network based on training data and a plurality of images. The training data is associated with a plurality of patients from at least one imaging device. The plurality of images is associated with a plurality of masks from a plurality of objects. The convolutional neural network comprises a decoder consisting of at least one up-sampling layer and at least one convolutional layer. The system also generates a loss function based on the plurality of masks, where the loss function is iteratively back propagated to tune parameters of the convolutional neural network. The system also predicts a classification label for an input image based on the convolutional neural network.

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